Computer architecture for emulating a differential amlpifier in a correlithm object processing system

ABSTRACT

A device includes a memory and a node engine. The memory stores first and second tables that values represented by correlithm objects. The first table provides an intermediate output value based on the difference between two input values. The second table provides an output value based on an amplification of the intermediate output value from the first table. The node engine receives a first input correlithm object and a second input correlithm object, and identifies an amplification correlithm object. The node engine determines an output correlithm object by using the first and second tables in conjunction with the first input correlithm object, the second input correlithm object, and the amplified value correlithm object. The output correlithm object is based on the amplified difference between the first input correlithm object and the second input correlithm object.

TECHNICAL FIELD

The present disclosure relates generally to computer architectures foremulating a processing system, and more specifically to a computerarchitecture for emulating a differential amplifier in a correlithmobject processing system.

BACKGROUND

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many security applications such asfacial recognition, voice recognition, and fraud detection.

Thus, it is desirable to provide a solution that allows computingsystems to efficiently determine how similar different data samples areto each other and to perform operations based on their similarity.

SUMMARY

Conventional computers are highly attuned to using operations thatrequire manipulating ordinal numbers, especially ordinal binaryintegers. The value of an ordinal number corresponds with its positionin a set of sequentially ordered number values. These computers useordinal binary integers to represent, manipulate, and store information.These computers rely on the numerical order of ordinal binary integersrepresenting data to perform various operations such as counting,sorting, indexing, and mathematical calculations. Even when performingoperations that involve other number systems (e.g. floating point),conventional computers still resort to using ordinal binary integers toperform any operations.

Ordinal based number systems only provide information about the sequenceorder of the numbers themselves based on their numeric values. Ordinalnumbers do not provide any information about any other types ofrelationships for the data being represented by the numeric values suchas similarity. For example, when a conventional computer uses ordinalnumbers to represent data samples (e.g. images or audio signals),different data samples are represented by different numeric values. Thedifferent numeric values do not provide any information about howsimilar or dissimilar one data sample is from another. Unless there isan exact match in ordinal number values, conventional systems are unableto tell if a data sample matches or is similar to any other datasamples. As a result, conventional computers are unable to use ordinalnumbers by themselves for comparing different data samples and insteadthese computers rely on complex signal processing techniques.Determining whether a data sample matches or is similar to other datasamples is not a trivial task and poses several technical challenges forconventional computers. These technical challenges result in complexprocesses that consume processing power which reduces the speed andperformance of the system. The ability to compare unknown data samplesto known data samples is crucial for many applications such as securityapplication (e.g. face recognition, voice recognition, and frauddetection).

The system described in the present application provides a technicalsolution that enables the system to efficiently determine how similardifferent objects are to each other and to perform operations based ontheir similarity. In contrast to conventional systems, the system usesan unconventional configuration to perform various operations usingcategorical numbers and geometric objects, also referred to ascorrelithm objects, instead of ordinal numbers. Using categoricalnumbers and correlithm objects on a conventional device involveschanging the traditional operation of the computer to supportrepresenting and manipulating concepts as correlithm objects. A deviceor system may be configured to implement or emulate a special purposecomputing device capable of performing operations using correlithmobjects. Implementing or emulating a correlithm object processing systemimproves the operation of a device by enabling the device to performnon-binary comparisons (i.e. match or no match) between different datasamples. This enables the device to quantify a degree of similaritybetween different data samples. This increases the flexibility of thedevice to work with data samples having different data types and/orformats, and also increases the speed and performance of the device whenperforming operations using data samples. These technical advantages andother improvements to the device are described in more detail throughoutthe disclosure.

In one embodiment, the system is configured to use binary integers ascategorical numbers rather than ordinal numbers which enables the systemto determine how similar a data sample is to other data samples.Categorical numbers provide information about similar or dissimilardifferent data samples are from each other. For example, categoricalnumbers can be used in facial recognition applications to representdifferent images of faces and/or features of the faces. The systemprovides a technical advantage by allowing the system to assigncorrelithm objects represented by categorical numbers to different datasamples based on how similar they are to other data samples. As anexample, the system is able to assign correlithm objects to differentimages of people such that the correlithm objects can be directly usedto determine how similar the people in the images are to each other. Inother words, the system can use correlithm objects in facial recognitionapplications to quickly determine whether a captured image of a personmatches any previously stored images without relying on conventionalsignal processing techniques.

Correlithm object processing systems use new types of data structurescalled correlithm objects that improve the way a device operates, forexample, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects are data structures designed to improve theway a device stores, retrieves, and compares data samples in memory.Correlithm objects also provide a data structure that is independent ofthe data type and format of the data samples they represent. Correlithmobjects allow data samples to be directly compared regardless of theiroriginal data type and/or format.

A correlithm object processing system uses a combination of a sensortable, a node table, and/or an actor table to provide a specific set ofrules that improve computer-related technologies by enabling devices tocompare and to determine the degree of similarity between different datasamples regardless of the data type and/or format of the data samplethey represent. The ability to directly compare data samples havingdifferent data types and/or formatting is a new functionality thatcannot be performed using conventional computing systems and datastructures.

In addition, a correlithm object processing system uses a combination ofa sensor table, a node table, and/or an actor table to provide aparticular manner for transforming data samples between ordinal numberrepresentations and correlithm objects in a correlithm object domain.Transforming data samples between ordinal number representations andcorrelithm objects involves fundamentally changing the data type of datasamples between an ordinal number system and a categorical number systemto achieve the previously described benefits of the correlithm objectprocessing system.

Using correlithm objects allows the system or device to compare datasamples (e.g. images) even when the input data sample does not exactlymatch any known or previously stored input values. For example, an inputdata sample that is an image may have different lighting conditions thanthe previously stored images. The differences in lighting conditions canmake images of the same person appear different from each other. Thedevice uses an unconventional configuration that implements a correlithmobject processing system that uses the distance between the data sampleswhich are represented as correlithm objects and other known data samplesto determine whether the input data sample matches or is similar to theother known data samples. Implementing a correlithm object processingsystem fundamentally changes the device and the traditional dataprocessing paradigm. Implementing the correlithm object processingsystem improves the operation of the device by enabling the device toperform non-binary comparisons of data samples. In other words, thedevice can determine how similar the data samples are to each other evenwhen the data samples are not exact matches. In addition, the device canquantify how similar data samples are to one another. The ability todetermine how similar data samples are to each other is unique anddistinct from conventional computers that can only perform binarycomparisons to identify exact matches.

A string correlithm object comprising a series of adjacent sub-stringcorrelithm objects whose cores overlap with each other to permit datavalues to be correlated with each other in n-dimensional space. Thedistance between adjacent sub-string correlithm objects can be selectedto create a tighter or looser correlation among the elements of thestring correlithm object in n-dimensional space. Thus, where data valueshave a pre-existing relationship with each other in the real-world,those relationships can be maintained in n-dimensional space if they arerepresented by sub-string correlithm objects of a string correlithmobject. In addition, new data values can be represented by sub-stringcorrelithm objects by interpolating the distance between those and otherdata values and representing that interpolation with sub-stringcorrelithm objects of a string correlithm object in n-dimensional space.The ability to migrate these relationships between data values in thereal world to relationships among correlithm objects provides asignificant advance in the ability to record, store, and faithfullyreproduce data within different computing environments. Furthermore, theuse of string correlithm objects significantly reduces the computationalburden of comparing time-varying sequences of data, or multi-dimensionaldata objects, with respect to conventional forms of executing dynamictime warping algorithms. The reduced computational burden results infaster processing speeds and reduced loads on memory structures used toperform the comparison of string correlithm objects.

The problems associated with comparing data sets and identifying matchesbased on the comparison are problems necessarily rooted in computertechnologies. As described above, conventional systems are limited to abinary comparison that can only determine whether an exact match isfound. Emulating a correlithm object processing system provides atechnical solution that addresses problems associated with comparingdata sets and identifying matches. Using correlithm objects to representdata samples fundamentally changes the operation of a device and how thedevice views data samples. By implementing a correlithm objectprocessing system, the device can determine the distance between thedata samples and other known data samples to determine whether the inputdata sample matches or is similar to the other known data samples. Inaddition, the device can determine a degree of similarity thatquantifies how similar different data samples are to one another.

Sub-string correlithm objects of a string correlithm object can be usedto perform mathematical operations using correlithm objects, whichfacilitates homomorphic computing. Homomorphic computing offers a way toperform computations in a distributed setting or in the cloud therebyaddressing many of the technical problems associated with storing,moving, and converting data back and forth between real-world values andcorrelithm objects. This increases processing speeds and reduces theamount of memory necessary for performing computations.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic view of an embodiment of a special purposecomputer implementing correlithm objects in an n-dimensional space;

FIG. 2 is a perspective view of an embodiment of a mapping betweencorrelithm objects in different n-dimensional spaces;

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system;

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow;

FIG. 5 is a schematic diagram of an embodiment a computer architecturefor emulating a correlithm object processing system;

FIG. 6 illustrates an embodiment of how a string correlithm object maybe implemented within a node by a device;

FIG. 7 illustrates another embodiment of how a string correlithm objectmay be implemented within a node by a device;

FIG. 8 is a schematic diagram of another embodiment of a deviceimplementing string correlithm objects in a node for a correlithm objectprocessing system;

FIG. 9 is an embodiment of a graph of a probability distribution formatching a random correlithm object with a particular correlithm object;

FIG. 10 is a schematic diagram of an embodiment of a device implementinga correlithm object core in a node for a correlithm object processingsystem;

FIG. 11 is an embodiment of a graph of probability distributions foradjacent root correlithm objects;

FIG. 12A is an embodiment of a string correlithm object generator;

FIG. 12B is an embodiment of a table demonstrating a change in bitvalues associated with sub-string correlithm objects;

FIG. 13 is an embodiment of a process for generating a string correlithmobject;

FIG. 14 is an embodiment of discrete data values mapped to sub-stringcorrelithm objects of a string correlithm object;

FIG. 15A is an embodiment of analog data values mapped to sub-stringcorrelithm objects of a string correlithm object;

FIG. 15B is an embodiment of a table demonstrating how to map analogdata values to sub-string correlithm objects using interpolation;

FIG. 16 is an embodiment of non-string correlithm objects mapped tosub-string correlithm objects of a string correlithm object;

FIG. 17 is an embodiment of a process for mapping non-string correlithmobjects to sub-string correlithm objects of a string correlithm object;

FIG. 18 is an embodiment of sub-string correlithm objects of a firststring correlithm object mapped to sub-string correlithm objects of asecond string correlithm objects;

FIG. 19 is an embodiment of a process for mapping sub-string correlithmobjects of a first string correlithm object to sub-string correlithmobjects of a second string correlithm objects;

FIG. 20 illustrates one embodiment of an actor that maps sub-stringcorrelithm objects of a string correlithm object to analog or discretedata values;

FIG. 21 is an embodiment of a process for mapping sub-string correlithmobjects of a string correlithm object to analog or discrete data values;

FIG. 22 is an embodiment of an integrator node engine in a correlithmobject processing system;

FIG. 23 is an embodiment of an integrator value table used by theintegrator node engine of FIG. 22;

FIG. 24 is an embodiment of a differentiator node engine in a correlithmobject processing system;

FIG. 25 is an embodiment of a differentiator value table used by thedifferentiator node of FIG. 24;

FIG. 26 is an embodiment of a differential amplifier node engine in acorrelithm object processing system;

FIG. 27A is an embodiment of a differential value table used by thedifferential amplifier node engine of FIG. 26; and

FIG. 27B is an embodiment of an amplifier value table used by thedifferential amplifier node engine of FIG. 26.

DETAILED DESCRIPTION

FIGS. 1-5 describe various embodiments of how a correlithm objectprocessing system may be implemented or emulated in hardware, such as aspecial purpose computer. FIGS. 6-19 describe various embodiments of howa correlithm object processing system can generate and use stringcorrelithm objects to record and faithfully playback data values. FIGS.20-27 describe various embodiments of how correlithm objects 104 can beused to represent digits of real-world numerical values and how toperform operations on correlithm objects 104 using string correlithmobjects 602.

FIG. 1 is a schematic view of an embodiment of a user device 100implementing correlithm objects 104 in an n-dimensional space 102.Examples of user devices 100 include, but are not limited to, desktopcomputers, mobile phones, tablet computers, laptop computers, or otherspecial purpose computer platform. The user device 100 is configured toimplement or emulate a correlithm object processing system that usescategorical numbers to represent data samples as correlithm objects 104in a high-dimensional space 102, for example a high-dimensional binarycube. Additional information about the correlithm object processingsystem is described in FIG. 3. Additional information about configuringthe user device 100 to implement or emulate a correlithm objectprocessing system is described in FIG. 5.

Conventional computers rely on the numerical order of ordinal binaryintegers representing data to perform various operations such ascounting, sorting, indexing, and mathematical calculations. Even whenperforming operations that involve other number systems (e.g. floatingpoint), conventional computers still resort to using ordinal binaryintegers to perform any operations. Ordinal based number systems onlyprovide information about the sequence order of the numbers themselvesbased on their numeric values. Ordinal numbers do not provide anyinformation about any other types of relationships for the data beingrepresented by the numeric values, such as similarity. For example, whena conventional computer uses ordinal numbers to represent data samples(e.g. images or audio signals), different data samples are representedby different numeric values. The different numeric values do not provideany information about how similar or dissimilar one data sample is fromanother. In other words, conventional computers are only able to makebinary comparisons of data samples which only results in determiningwhether the data samples match or do not match. Unless there is an exactmatch in ordinal number values, conventional systems are unable to tellif a data sample matches or is similar to any other data samples. As aresult, conventional computers are unable to use ordinal numbers bythemselves for determining similarity between different data samples,and instead these computers rely on complex signal processingtechniques. Determining whether a data sample matches or is similar toother data samples is not a trivial task and poses several technicalchallenges for conventional computers. These technical challenges resultin complex processes that consume processing power which reduces thespeed and performance of the system.

In contrast to conventional systems, the user device 100 operates as aspecial purpose machine for implementing or emulating a correlithmobject processing system. Implementing or emulating a correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons (i.e.match or no match) between different data samples. This enables the userdevice 100 to quantify a degree of similarity between different datasamples. This increases the flexibility of the user device 100 to workwith data samples having different data types and/or formats, and alsoincreases the speed and performance of the user device 100 whenperforming operations using data samples. These improvements and otherbenefits to the user device 100 are described in more detail below andthroughout the disclosure.

For example, the user device 100 employs the correlithm objectprocessing system to allow the user device 100 to compare data sampleseven when the input data sample does not exactly match any known orpreviously stored input values. Implementing a correlithm objectprocessing system fundamentally changes the user device 100 and thetraditional data processing paradigm. Implementing the correlithm objectprocessing system improves the operation of the user device 100 byenabling the user device 100 to perform non-binary comparisons of datasamples. In other words, the user device 100 is able to determine howsimilar the data samples are to each other even when the data samplesare not exact matches. In addition, the user device 100 is able toquantify how similar data samples are to one another. The ability todetermine how similar data samples are to each other is unique anddistinct from conventional computers that can only perform binarycomparisons to identify exact matches.

The user device's 100 ability to perform non-binary comparisons of datasamples also fundamentally changes traditional data searching paradigms.For example, conventional search engines rely on finding exact matchesor exact partial matches of search tokens to identify related datasamples. For instance, conventional text-based search engines arelimited to finding related data samples that have text that exactlymatches other data samples. These search engines only provide a binaryresult that identifies whether or not an exact match was found based onthe search token. Implementing the correlithm object processing systemimproves the operation of the user device 100 by enabling the userdevice 100 to identify related data samples based on how similar thesearch token is to other data sample. These improvements result inincreased flexibility and faster search times when using a correlithmobject processing system. The ability to identify similarities betweendata samples expands the capabilities of a search engine to include datasamples that may not have an exact match with a search token but arestill related and similar in some aspects. The user device 100 is alsoable to quantify how similar data samples are to each other based oncharacteristics besides exact matches to the search token. Implementingthe correlithm object processing system involves operating the userdevice 100 in an unconventional manner to achieve these technologicalimprovements as well as other benefits described below for the userdevice 100.

Computing devices typically rely on the ability to compare data sets(e.g. data samples) to one another for processing. For example, insecurity or authentication applications a computing device is configuredto compare an input of an unknown person to a data set of known people(or biometric information associated with these people). The problemsassociated with comparing data sets and identifying matches based on thecomparison are problems necessarily rooted in computer technologies. Asdescribed above, conventional systems are limited to a binary comparisonthat can only determine whether an exact match is found. As an example,an input data sample that is an image of a person may have differentlighting conditions than previously stored images. In this example,different lighting conditions can make images of the same person appeardifferent from each other. Conventional computers are unable todistinguish between two images of the same person with differentlighting conditions and two images of two different people withoutcomplicated signal processing. In both of these cases, conventionalcomputers can only determine that the images are different. This isbecause conventional computers rely on manipulating ordinal numbers forprocessing.

In contrast, the user device 100 uses an unconventional configurationthat uses correlithm objects to represent data samples. Using correlithmobjects to represent data samples fundamentally changes the operation ofthe user device 100 and how the device views data samples. Byimplementing a correlithm object processing system, the user device 100can determine the distance between the data samples and other known datasamples to determine whether the input data sample matches or is similarto the other known data samples, as explained in detail below. Unlikethe conventional computers described in the previous example, the userdevice 100 is able to distinguish between two images of the same personwith different lighting conditions and two images of two differentpeople by using correlithm objects 104. Correlithm objects allow theuser device 100 to determine whether there are any similarities betweendata samples, such as between two images that are different from eachother in some respects but similar in other respects. For example, theuser device 100 is able to determine that despite different lightingconditions, the same person is present in both images.

In addition, the user device 100 is able to determine a degree ofsimilarity that quantifies how similar different data samples are to oneanother. Implementing a correlithm object processing system in the userdevice 100 improves the operation of the user device 100 when comparingdata sets and identifying matches by allowing the user device 100 toperform non-binary comparisons between data sets and to quantify thesimilarity between different data samples. In addition, using acorrelithm object processing system results in increased flexibility andfaster search times when comparing data samples or data sets. Thus,implementing a correlithm object processing system in the user device100 provides a technical solution to a problem necessarily rooted incomputer technologies.

The ability to implement a correlithm object processing system providesa technical advantage by allowing the system to identify and comparedata samples regardless of whether an exact match has been previousobserved or stored. In other words, using the correlithm objectprocessing system the user device 100 is able to identify similar datasamples to an input data sample in the absence of an exact match. Thisfunctionality is unique and distinct from conventional computers thatcan only identify data samples with exact matches.

Examples of data samples include, but are not limited to, images, files,text, audio signals, biometric signals, electric signals, or any othersuitable type of data. A correlithm object 104 is a point in then-dimensional space 102, sometimes called an “n-space.” The value ofrepresents the number of dimensions of the space. For example, ann-dimensional space 102 may be a 3-dimensional space, a 50-dimensionalspace, a 100-dimensional space, or any other suitable dimension space.The number of dimensions depends on its ability to support certainstatistical tests, such as the distances between pairs of randomlychosen points in the space approximating a normal distribution. In someembodiments, increasing the number of dimensions in the n-dimensionalspace 102 modifies the statistical properties of the system to provideimproved results. Increasing the number of dimensions increases theprobability that a correlithm object 104 is similar to other adjacentcorrelithm objects 104. In other words, increasing the number ofdimensions increases the correlation between how close a pair ofcorrelithm objects 104 are to each other and how similar the correlithmobjects 104 are to each other.

Correlithm object processing systems use new types of data structurescalled correlithm objects 104 that improve the way a device operates,for example, by enabling the device to perform non-binary data setcomparisons and to quantify the similarity between different datasamples. Correlithm objects 104 are data structures designed to improvethe way a device stores, retrieves, and compares data samples in memory.Unlike conventional data structures, correlithm objects 104 are datastructures where objects can be expressed in a high-dimensional spacesuch that distance 106 between points in the space represent thesimilarity between different objects or data samples. In other words,the distance 106 between a pair of correlithm objects 104 in then-dimensional space 102 indicates how similar the correlithm objects 104are from each other and the data samples they represent. Correlithmobjects 104 that are close to each other are more similar to each otherthan correlithm objects 104 that are further apart from each other. Forexample, in a facial recognition application, correlithm objects 104used to represent images of different types of glasses may be relativelyclose to each other compared to correlithm objects 104 used to representimages of other features such as facial hair. An exact match between twodata samples occurs when their corresponding correlithm objects 104 arethe same or have no distance between them. When two data samples are notexact matches but are similar, the distance between their correlithmobjects 104 can be used to indicate their similarities. In other words,the distance 106 between correlithm objects 104 can be used to identifyboth data samples that exactly match each other as well as data samplesthat do not match but are similar. This feature is unique to acorrelithm processing system and is unlike conventional computers thatare unable to detect when data samples are different but similar in someaspects.

Correlithm objects 104 also provide a data structure that is independentof the data type and format of the data samples they represent.Correlithm objects 104 allow data samples to be directly comparedregardless of their original data type and/or format. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. For example, comparing images using conventional data structuresinvolves significant amounts of image processing which is time consumingand consumes processing resources. Thus, using correlithm objects 104 torepresent data samples provides increased flexibility and improvedperformance compared to using other conventional data structures.

In one embodiment, correlithm objects 104 may be represented usingcategorical binary strings. The number of bits used to represent thecorrelithm object 104 corresponds with the number of dimensions of then-dimensional space 102 where the correlithm object 102 is located. Forexample, each correlithm object 104 may be uniquely identified using a64-bit string in a 64-dimensional space 102. As another example, eachcorrelithm object 104 may be uniquely identified using a 10-bit stringin a 10-dimensional space 102. In other examples, correlithm objects 104can be identified using any other suitable number of bits in a stringthat corresponds with the number of dimensions in the n-dimensionalspace 102.

In this configuration, the distance 106 between two correlithm objects104 can be determined based on the differences between the bits of thetwo correlithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between the correlithm objects 104. The distance 106 between twocorrelithm objects 104 can be computed using Hamming distance,anti-Hamming distance or any other suitable technique.

As an example, using a 10-dimensional space 102, a first correlithmobject 104 is represented by a first 10-bit string (1001011011) and asecond correlithm object 104 is represented by a second 10-bit string(1000011011). The Hamming distance corresponds with the number of bitsthat differ between the first correlithm object 104 and the secondcorrelithm object 104. Conversely, the anti-Hamming distance correspondswith the number of bits that are alike between the first correlithmobject 104 and the second correlithm object 104. Thus, the Hammingdistance between the first correlithm object 104 and the secondcorrelithm object 104 can be computed as follows:

1001011011

1000011011

. . .

0001000000

In this example, the Hamming distance is equal to one because only onebit differs between the first correlithm object 104 and the secondcorrelithm object 104. Conversely, the anti-Hamming distance is ninebecause nine bits are the same between the first and second correlithmobjects 104. As another example, a third correlithm object 104 isrepresented by a third 10-bit string (0110100100). In this example, theHamming distance between the first correlithm object 104 and the thirdcorrelithm object 104 can be computed as follows:

1001011011

0110100100

. . .

1111111111

The Hamming distance is equal to ten because all of the bits aredifferent between the first correlithm object 104 and the thirdcorrelithm object 104. Conversely, the anti-Hamming distance is zerobecause none of the bits are the same between the first and thirdcorrelithm objects 104. In the previous example, a Hamming distanceequal to one indicates that the first correlithm object 104 and thesecond correlithm object 104 are close to each other in then-dimensional space 102, which means they are similar to each other.Similarly, an anti-Hamming distance equal to nine also indicates thatthe first and second correlithm objects are close to each other inn-dimensional space 102, which also means they are similar to eachother. In the second example, a Hamming distance equal to ten indicatesthat the first correlithm object 104 and the third correlithm object 104are further from each other in the n-dimensional space 102 and are lesssimilar to each other than the first correlithm object 104 and thesecond correlithm object 104. Similarly, an anti-Hamming distance equalto zero also indicates that that the first and third correlithm objects104 are further from each other in n-dimensional space 102 and are lesssimilar to each other than the first and second correlithm objects 104.In other words, the similarity between a pair of correlithm objects canbe readily determined based on the distance between the pair correlithmobjects, as represented by either Hamming distances or anti-Hammingdistances.

As another example, the distance between a pair of correlithm objects104 can be determined by performing an XOR operation between the pair ofcorrelithm objects 104 and counting the number of logical high values inthe binary string. The number of logical high values indicates thenumber of bits that are different between the pair of correlithm objects104 which also corresponds with the Hamming distance between the pair ofcorrelithm objects 104.

In another embodiment, the distance 106 between two correlithm objects104 can be determined using a Minkowski distance such as the Euclideanor “straight-line” distance between the correlithm objects 104. Forexample, the distance 106 between a pair of correlithm objects 104 maybe determined by calculating the square root of the sum of squares ofthe coordinate difference in each dimension.

The user device 100 is configured to implement or emulate a correlithmobject processing system that comprises one or more sensors 302, nodes304, and/or actors 306 in order to convert data samples betweenreal-world values or representations and to correlithm objects 104 in acorrelithm object domain. Sensors 302 are generally configured toconvert real-world data samples to the correlithm object domain. Nodes304 are generally configured to process or perform various operations oncorrelithm objects in the correlithm object domain. Actors 306 aregenerally configured to convert correlithm objects 104 into real-worldvalues or representations. Additional information about sensors 302,nodes 304, and actors 306 is described in FIG. 3.

Performing operations using correlithm objects 104 in a correlithmobject domain allows the user device 100 to identify relationshipsbetween data samples that cannot be identified using conventional dataprocessing systems. For example, in the correlithm object domain, theuser device 100 is able to identify not only data samples that exactlymatch an input data sample, but also other data samples that havesimilar characteristics or features as the input data samples.Conventional computers are unable to identify these types ofrelationships readily. Using correlithm objects 104 improves theoperation of the user device 100 by enabling the user device 100 toefficiently process data samples and identify relationships between datasamples without relying on signal processing techniques that require asignificant amount of processing resources. These benefits allow theuser device 100 to operate more efficiently than conventional computersby reducing the amount of processing power and resources that are neededto perform various operations.

FIG. 2 is a schematic view of an embodiment of a mapping betweencorrelithm objects 104 in different n-dimensional spaces 102. Whenimplementing a correlithm object processing system, the user device 100performs operations within the correlithm object domain using correlithmobjects 104 in different n-dimensional spaces 102. As an example, theuser device 100 may convert different types of data samples havingreal-world values into correlithm objects 104 in different n-dimensionalspaces 102. For instance, the user device 100 may convert data samplesof text into a first set of correlithm objects 104 in a firstn-dimensional space 102 and data samples of audio samples as a secondset of correlithm objects 104 in a second n-dimensional space 102.Conventional systems require data samples to be of the same type and/orformat to perform any kind of operation on the data samples. In someinstances, some types of data samples cannot be compared because thereis no common format available. For example, conventional computers areunable to compare data samples of images and data samples of audiosamples because there is no common format. In contrast, the user device100 implementing a correlithm object processing system is able tocompare and perform operations using correlithm objects 104 in thecorrelithm object domain regardless of the type or format of theoriginal data samples.

In FIG. 2, a first set of correlithm objects 104A are defined within afirst n-dimensional space 102A and a second set of correlithm objects104B are defined within a second n-dimensional space 102B. Then-dimensional spaces may have the same number of dimensions or adifferent number of dimensions. For example, the first n-dimensionalspace 102A and the second n-dimensional space 102B may both be threedimensional spaces. As another example, the first n-dimensional space102A may be a three-dimensional space and the second n-dimensional space102B may be a nine-dimensional space. Correlithm objects 104 in thefirst n-dimensional space 102A and second n-dimensional space 102B aremapped to each other. In other words, a correlithm object 104A in thefirst n-dimensional space 102A may reference or be linked with aparticular correlithm object 104B in the second n-dimensional space102B. The correlithm objects 104 may also be linked with and referencedwith other correlithm objects 104 in other n-dimensional spaces 102.

In one embodiment, a data structure such as table 200 may be used to mapor link correlithm objects 104 in different n-dimensional spaces 102. Insome instances, table 200 is referred to as a node table. Table 200 isgenerally configured to identify a first plurality of correlithm objects104 in a first n-dimensional space 102 and a second plurality ofcorrelithm objects 104 in a second n-dimensional space 102. Eachcorrelithm object 104 in the first n-dimensional space 102 is linkedwith a correlithm object 104 is the second n-dimensional space 102. Forexample, table 200 may be configured with a first column 202 that listscorrelithm objects 104A as source correlithm objects and a second column204 that lists corresponding correlithm objects 104B as targetcorrelithm objects. In other examples, table 200 may be configured inany other suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe used to convert between a correlithm object 104 in a firstn-dimensional space and a correlithm object 104 is a secondn-dimensional space.

FIG. 3 is a schematic view of an embodiment of a correlithm objectprocessing system 300 that is implemented by a user device 100 toperform operations using correlithm objects 104. The system 300generally comprises a sensor 302, a node 304, and an actor 306. Thesystem 300 may be configured with any suitable number and/orconfiguration of sensors 302, nodes 304, and actors 306. An example ofthe system 300 in operation is described in FIG. 4. In one embodiment, asensor 302, a node 304, and an actor 306 may all be implemented on thesame device (e.g. user device 100). In other embodiments, a sensor 302,a node 304, and an actor 306 may each be implemented on differentdevices in signal communication with each other for example over anetwork. In other embodiments, different devices may be configured toimplement any combination of sensors 302, nodes 304, and actors 306.

Sensors 302 serve as interfaces that allow a user device 100 to convertreal-world data samples into correlithm objects 104 that can be used inthe correlithm object domain. Sensors 302 enable the user device 100 tocompare and perform operations using correlithm objects 104 regardlessof the data type or format of the original data sample. Sensors 302 areconfigured to receive a real-world value 320 representing a data sampleas an input, to determine a correlithm object 104 based on thereal-world value 320, and to output the correlithm object 104. Forexample, the sensor 302 may receive an image 301 of a person and outputa correlithm object 322 to the node 304 or actor 306. In one embodiment,sensors 302 are configured to use sensor tables 308 that link aplurality of real-world values with a plurality of correlithm objects104 in an n-dimensional space 102. Real-world values are any type ofsignal, value, or representation of data samples. Examples of real-worldvalues include, but are not limited to, images, pixel values, text,audio signals, electrical signals, and biometric signals. As an example,a sensor table 308 may be configured with a first column 312 that listsreal-world value entries corresponding with different images and asecond column 314 that lists corresponding correlithm objects 104 asinput correlithm objects. In other examples, sensor tables 308 may beconfigured in any other suitable manner or may be implemented using anyother suitable data structure. In some embodiments, one or more mappingfunctions may be used to translate between a real-world value 320 and acorrelithm object 104 in an n-dimensional space. Additional informationfor implementing or emulating a sensor 302 in hardware is described inFIG. 5.

Nodes 304 are configured to receive a correlithm object 104 (e.g. aninput correlithm object 104), to determine another correlithm object 104based on the received correlithm object 104, and to output theidentified correlithm object 104 (e.g. an output correlithm object 104).In one embodiment, nodes 304 are configured to use node tables 200 thatlink a plurality of correlithm objects 104 from a first n-dimensionalspace 102 with a plurality of correlithm objects 104 in a secondn-dimensional space 102. A node table 200 may be configured similar tothe table 200 described in FIG. 2. Additional information forimplementing or emulating a node 304 in hardware is described in FIG. 5.

Actors 306 serve as interfaces that allow a user device 100 to convertcorrelithm objects 104 in the correlithm object domain back toreal-world values or data samples. Actors 306 enable the user device 100to convert from correlithm objects 104 into any suitable type ofreal-world value. Actors 306 are configured to receive a correlithmobject 104 (e.g. an output correlithm object 104), to determine areal-world output value 326 based on the received correlithm object 104,and to output the real-world output value 326. The real-world outputvalue 326 may be a different data type or representation of the originaldata sample. As an example, the real-world input value 320 may be animage 301 of a person and the resulting real-world output value 326 maybe text 327 and/or an audio signal identifying the person. In oneembodiment, actors 306 are configured to use actor tables 310 that linka plurality of correlithm objects 104 in an n-dimensional space 102 witha plurality of real-world values. As an example, an actor table 310 maybe configured with a first column 316 that lists correlithm objects 104as output correlithm objects and a second column 318 that listsreal-world values. In other examples, actor tables 310 may be configuredin any other suitable manner or may be implemented using any othersuitable data structure. In some embodiments, one or more mappingfunctions may be employed to translate between a correlithm object 104in an n-dimensional space and a real-world output value 326. Additionalinformation for implementing or emulating an actor 306 in hardware isdescribed in FIG. 5.

A correlithm object processing system 300 uses a combination of a sensortable 308, a node table 200, and/or an actor table 310 to provide aspecific set of rules that improve computer-related technologies byenabling devices to compare and to determine the degree of similaritybetween different data samples regardless of the data type and/or formatof the data sample they represent. The ability to directly compare datasamples having different data types and/or formatting is a newfunctionality that cannot be performed using conventional computingsystems and data structures. Conventional systems require data samplesto be of the same type and/or format in order to perform any kind ofoperation on the data samples. In some instances, some types of datasamples are incompatible with each other and cannot be compared becausethere is no common format available. For example, conventional computersare unable to compare data samples of images with data samples of audiosamples because there is no common format available. In contrast, adevice implementing a correlithm object processing system uses acombination of a sensor table 308, a node table 200, and/or an actortable 310 to compare and perform operations using correlithm objects 104in the correlithm object domain regardless of the type or format of theoriginal data samples. The correlithm object processing system 300 usesa combination of a sensor table 308, a node table 200, and/or an actortable 310 as a specific set of rules that provides a particular solutionto dealing with different types of data samples and allows devices toperform operations on different types of data samples using correlithmobjects 104 in the correlithm object domain. In some instances,comparing data samples as correlithm objects 104 is computationally moreefficient and faster than comparing data samples in their originalformat. Thus, using correlithm objects 104 to represent data samplesprovides increased flexibility and improved performance compared tousing other conventional data structures. The specific set of rules usedby the correlithm object processing system 300 go beyond simply usingroutine and conventional activities in order to achieve this newfunctionality and performance improvements.

In addition, correlithm object processing system 300 uses a combinationof a sensor table 308, a node table 200, and/or an actor table 310 toprovide a particular manner for transforming data samples betweenordinal number representations and correlithm objects 104 in acorrelithm object domain. For example, the correlithm object processingsystem 300 may be configured to transform a representation of a datasample into a correlithm object 104, to perform various operations usingthe correlithm object 104 in the correlithm object domain, and totransform a resulting correlithm object 104 into another representationof a data sample. Transforming data samples between ordinal numberrepresentations and correlithm objects 104 involves fundamentallychanging the data type of data samples between an ordinal number systemand a categorical number system to achieve the previously describedbenefits of the correlithm object processing system 300.

FIG. 4 is a protocol diagram of an embodiment of a correlithm objectprocess flow 400. A user device 100 implements process flow 400 toemulate a correlithm object processing system 300 to perform operationsusing correlithm object 104 such as facial recognition. The user device100 implements process flow 400 to compare different data samples (e.g.images, voice signals, or text) to each other and to identify otherobjects based on the comparison. Process flow 400 provides instructionsthat allows user devices 100 to achieve the improved technical benefitsof a correlithm object processing system 300.

Conventional systems are configured to use ordinal numbers foridentifying different data samples. Ordinal based number systems onlyprovide information about the sequence order of numbers based on theirnumeric values, and do not provide any information about any other typesof relationships for the data samples being represented by the numericvalues such as similarity. In contrast, a user device 100 can implementor emulate the correlithm object processing system 300 which provides anunconventional solution that uses categorical numbers and correlithmobjects 104 to represent data samples. For example, the system 300 maybe configured to use binary integers as categorical numbers to generatecorrelithm objects 104 which enables the user device 100 to performoperations directly based on similarities between different datasamples. Categorical numbers provide information about how similardifferent data sample are from each other. Correlithm objects 104generated using categorical numbers can be used directly by the system300 for determining how similar different data samples are from eachother without relying on exact matches, having a common data type orformat, or conventional signal processing techniques.

A non-limiting example is provided to illustrate how the user device 100implements process flow 400 to emulate a correlithm object processingsystem 300 to perform facial recognition on an image to determine theidentity of the person in the image. In other examples, the user device100 may implement process flow 400 to emulate a correlithm objectprocessing system 300 to perform voice recognition, text recognition, orany other operation that compares different objects.

At step 402, a sensor 302 receives an input signal representing a datasample. For example, the sensor 302 receives an image of person's faceas a real-world input value 320. The input signal may be in any suitabledata type or format. In one embodiment, the sensor 302 may obtain theinput signal in real-time from a peripheral device (e.g. a camera). Inanother embodiment, the sensor 302 may obtain the input signal from amemory or database.

At step 404, the sensor 302 identifies a real-world value entry in asensor table 308 based on the input signal. In one embodiment, thesystem 300 identifies a real-world value entry in the sensor table 308that matches the input signal. For example, the real-world value entriesmay comprise previously stored images. The sensor 302 may compare thereceived image to the previously stored images to identify a real-worldvalue entry that matches the received image. In one embodiment, when thesensor 302 does not find an exact match, the sensor 302 finds areal-world value entry that most closely matches the received image.

At step 406, the sensor 302 identifies and fetches an input correlithmobject 104 in the sensor table 308 linked with the real-world valueentry. At step 408, the sensor 302 sends the identified input correlithmobject 104 to the node 304. In one embodiment, the identified inputcorrelithm object 104 is represented in the sensor table 308 using acategorical binary integer string. The sensor 302 sends the binarystring representing to the identified input correlithm object 104 to thenode 304.

At step 410, the node 304 receives the input correlithm object 104 anddetermines distances 106 between the input correlithm object 104 andeach source correlithm object 104 in a node table 200. In oneembodiment, the distance 106 between two correlithm objects 104 can bedetermined based on the differences between the bits of the twocorrelithm objects 104. In other words, the distance 106 between twocorrelithm objects can be determined based on how many individual bitsdiffer between a pair of correlithm objects 104. The distance 106between two correlithm objects 104 can be computed using Hammingdistance or any other suitable technique. In another embodiment, thedistance 106 between two correlithm objects 104 can be determined usinga Minkowski distance such as the Euclidean or “straight-line” distancebetween the correlithm objects 104. For example, the distance 106between a pair of correlithm objects 104 may be determined bycalculating the square root of the sum of squares of the coordinatedifference in each dimension.

At step 412, the node 304 identifies a source correlithm object 104 fromthe node table 200 with the shortest distance 106. A source correlithmobject 104 with the shortest distance from the input correlithm object104 is a correlithm object 104 either matches or most closely matchesthe received input correlithm object 104.

At step 414, the node 304 identifies and fetches a target correlithmobject 104 in the node table 200 linked with the source correlithmobject 104. At step 416, the node 304 outputs the identified targetcorrelithm object 104 to the actor 306. In this example, the identifiedtarget correlithm object 104 is represented in the node table 200 usinga categorical binary integer string. The node 304 sends the binarystring representing to the identified target correlithm object 104 tothe actor 306.

At step 418, the actor 306 receives the target correlithm object 104 anddetermines distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310. The actor 306 maycompute the distances between the target correlithm object 104 and eachoutput correlithm object 104 in an actor table 310 using a processsimilar to the process described in step 410.

At step 420, the actor 306 identifies an output correlithm object 104from the actor table 310 with the shortest distance 106. An outputcorrelithm object 104 with the shortest distance from the targetcorrelithm object 104 is a correlithm object 104 either matches or mostclosely matches the received target correlithm object 104.

At step 422, the actor 306 identifies and fetches a real-world outputvalue in the actor table 310 linked with the output correlithm object104. The real-world output value may be any suitable type of data samplethat corresponds with the original input signal. For example, thereal-world output value may be text that indicates the name of theperson in the image or some other identifier associated with the personin the image. As another example, the real-world output value may be anaudio signal or sample of the name of the person in the image. In otherexamples, the real-world output value may be any other suitablereal-world signal or value that corresponds with the original inputsignal. The real-world output value may be in any suitable data type orformat.

At step 424, the actor 306 outputs the identified real-world outputvalue. In one embodiment, the actor 306 may output the real-world outputvalue in real-time to a peripheral device (e.g. a display or a speaker).In one embodiment, the actor 306 may output the real-world output valueto a memory or database. In one embodiment, the real-world output valueis sent to another sensor 302. For example, the real-world output valuemay be sent to another sensor 302 as an input for another process.

FIG. 5 is a schematic diagram of an embodiment of a computerarchitecture 500 for emulating a correlithm object processing system300, 2200, 2400, and/or 2600 in a user device 100. The computerarchitecture 500 comprises a processor 502, a memory 504, a networkinterface 506, and an input-output (I/O) interface 508. The computerarchitecture 500 may be configured as shown, or in any other suitableconfiguration.

The processor 502 comprises one or more processors operably coupled tothe memory 504. The processor 502 is any electronic circuitry including,but not limited to, state machines, one or more central processing unit(CPU) chips, logic units, cores (e.g. a multi-core processor),field-programmable gate array (FPGAs), application specific integratedcircuits (ASICs), graphics processing units (GPUs), or digital signalprocessors (DSPs). The processor 502 may be a programmable logic device,a microcontroller, a microprocessor, or any suitable combination of thepreceding. The processor 502 is communicatively coupled to and in signalcommunication with the memory 204. The one or more processors areconfigured to process data and may be implemented in hardware orsoftware. For example, the processor 502 may be 8-bit, 16-bit, 32-bit,64-bit or of any other suitable architecture. The processor 502 mayinclude an arithmetic logic unit (ALU) for performing arithmetic andlogic operations, processor registers that supply operands to the ALUand store the results of ALU operations, and a control unit that fetchesinstructions from memory and executes them by directing the coordinatedoperations of the ALU, registers and other components.

The one or more processors 502 are configured to implement variousinstructions. For example, the one or more processors 502 are configuredto execute instructions to implement sensor engines 510, node engines512, actor engines 514, logic engines 515, and string correlithm objectengine 522. In an embodiment, sensor engines 510, node engines 512,actor engines 514, logic engines 515, and string correlithm objectengine 522 are implemented using logic units, FPGAs, ASICs, DSPs, or anyother suitable hardware. The sensor engines 510, node engines 512, actorengines 514, logic engines 515, and string correlithm object engine 522are each configured to implement a specific set of rules or processesthat provides an improved technological result.

In one embodiment, sensor engine 510 is configured to implement sensors302 that receive a real-world value 320 as an input, determine acorrelithm object 104 based on the real-world value 320, and outputcorrelithm object 104. An example operation of a sensor 302 implementedby a sensor engine 510 is described in FIG. 4 and FIG. 22.

In one embodiment, node engine 512 is configured to implement nodes 304that receive a correlithm object 104 (e.g. an input correlithm object104), determine another correlithm object 104 based on the receivedcorrelithm object 104, and output the identified correlithm object 104(e.g. an output correlithm object 104). A node 304 implemented by a nodeengine 512 is also configured to compute n-dimensional distances betweenpairs of correlithm objects 104. An example operation of a node 304implemented by a node engine 512 is described in FIG. 4.

In one embodiment, actor engine 514 is configured to implement actors306 that receive a correlithm object 104 (e.g. an output correlithmobject 104), determine a real-world output value 326 based on thereceived correlithm object 104, and output the real-world output value326. An example operation of an actor 306 implemented by an actor engine514 is described in FIG. 4 and FIG. 23.

In one embodiment, logic engines 515 are configured to implement anintegrator node engine 2201, a differentiator node engine 2401, and adifferential amplifier engine 2601, as illustrated in FIGS. 22, 24, and26.

In one embodiment, string correlithm object engine 522 is configured toimplement a string correlithm object generator 1200 and otherwiseprocess string correlithm objects 602 as described, for example, inconjunction with FIGS. 12-23.

The memory 504 comprises one or more non-transitory disks, tape drives,or solid-state drives, and may be used as an over-flow data storagedevice, to store programs when such programs are selected for execution,and to store instructions and data that are read during programexecution. The memory 504 may be volatile or non-volatile and maycomprise read-only memory (ROM), random-access memory (RAM), ternarycontent-addressable memory (TCAM), dynamic random-access memory (DRAM),and static random-access memory (SRAM). The memory 504 is operable tostore sensor instructions 516, node instructions 518, actor instructions520, sensor tables 308, node tables 200, actor tables 310, stringcorrelithm object tables 1220, 1400, 1500, 1520, 1600, and 1820, and/orany other data or instructions, such as value tables 2208, 2408, 2608,and 2610. The sensor instructions 516, node instructions 518, and actorinstructions 520 comprise any suitable set of instructions, logic,rules, or code operable to execute sensor engine 510, node engine 512,and actor engine 514 respectively.

The sensor tables 308, node tables 200, and actor tables 310 may beconfigured similar to sensor tables 308, node tables 200, and actortables 310 described in FIG. 3, respectively.

The network interface 506 is configured to enable wired and/or wirelesscommunications. The network interface 506 is configured to communicatedata with any other device or system. For example, the network interface506 may be configured for communication with a modem, a switch, arouter, a bridge, a server, or a client. The processor 502 is configuredto send and receive data using the network interface 506.

The I/O interface 508 may comprise ports, transmitters, receivers,transceivers, or any other devices for transmitting and/or receivingdata with peripheral devices as would be appreciated by one of ordinaryskill in the art upon viewing this disclosure. For example, the I/Ointerface 508 may be configured to communicate data between theprocessor 502 and peripheral hardware such as a graphical userinterface, a display, a mouse, a keyboard, a key pad, and a touch sensor(e.g. a touch screen).

FIGS. 6 and 7 are schematic diagrams of an embodiment of a device 100implementing string correlithm objects 602 for a correlithm objectprocessing system 300. String correlithm objects 602 can be used by acorrelithm object processing system 300 to embed higher orders ofcorrelithm objects 104 within lower orders of correlithm objects 104.The order of a correlithm object 104 depends on the number of bits usedto represent the correlithm object 104. The order of a correlithm object104 also corresponds with the number of dimensions in the n-dimensionalspace 102 where the correlithm object 104 is located. For example, acorrelithm object 104 represented by a 64-bit string is a higher ordercorrelithm object 104 than a correlithm object 104 represented by 16-bitstring.

Conventional computing systems rely on accurate data input and areunable to detect or correct for data input errors in real time. Forexample, a conventional computing device assumes a data stream iscorrect even when the data stream has bit errors. When a bit erroroccurs that leads to an unknown data value, the conventional computingdevice is unable to resolve the error without manual intervention. Incontrast, string correlithm objects 602 enable a device 100 to performoperations such as error correction and interpolation within thecorrelithm object processing system 300. For example, higher ordercorrelithm objects 104 can be used to associate an input correlithmobject 104 with a lower order correlithm 104 when an input correlithmobject does not correspond with a particular correlithm object 104 in ann-dimensional space 102. The correlithm object processing system 300uses the embedded higher order correlithm objects 104 to definecorrelithm objects 104 between the lower order correlithm objects 104which allows the device 100 to identify a correlithm object 104 in thelower order correlithm objects n-dimensional space 102 that correspondswith the input correlithm object 104. Using string correlithm objects602, the correlithm object processing system 300 is able to interpolateand/or to compensate for errors (e.g. bit errors) which improve thefunctionality of the correlithm object processing system 300 and theoperation of the device 100.

In some instances, string correlithm objects 602 may be used torepresent a series of data samples or temporal data samples. Forexample, a string correlithm object 602 may be used to represent audioor video segments. In this example, media segments are represented bysequential correlithm objects that are linked together using a stringcorrelithm object 602.

FIG. 6 illustrates an embodiment of how a string correlithm object 602may be implemented within a node 304 by a device 100. In otherembodiments, string correlithm objects 602 may be integrated within asensor 302 or an actor 306. In 32-dimensional space 102 where correlithmobjects 104 can be represented by a 32-bit string, the 32-bit string canbe embedded and used to represent correlithm objects 104 in a lowerorder 3-dimensional space 102 which uses three bits. The 32-bit stringscan be partitioned into three 12-bit portions, where each portioncorresponds with one of the three bits in the 3-dimensional space 102.For example, the correlithm object 104 represented by the 3-bit binaryvalue of 000 may be represented by a 32-bit binary string of zeros andthe correlithm object represented by the binary value of 111 may berepresented by a 32-bit string of all ones. As another example, thecorrelithm object 104 represented by the 3-bit binary value of 100 maybe represented by a 32-bit binary string with 12 bits set to onefollowed by 24 bits set to zero. In other examples, string correlithmobjects 602 can be used to embed any other combination and/or number ofn-dimensional spaces 102.

In one embodiment, when a higher order n-dimensional space 102 isembedded in a lower order n-dimensional space 102, one or morecorrelithm objects 104 are present in both the lower order n-dimensionalspace 102 and the higher order n-dimensional space 102. Correlithmobjects 104 that are present in both the lower order n-dimensional space102 and the higher order n-dimensional space 102 may be referred to asparent correlithm objects 603. Correlithm objects 104 in the higherorder n-dimensional space 102 may be referred to as child correlithmobjects 604. In this example, the correlithm objects 104 in the3-dimensional space 102 may be referred to as parent correlithm objects603 while the correlithm objects 104 in the 32-dimensional space 102 maybe referred to as child correlithm objects 604. In general, childcorrelithm objects 604 are represented by a higher order binary stringthan parent correlithm objects 603. In other words, the bit strings usedto represent a child correlithm object 604 may have more bits than thebit strings used to represent a parent correlithm object 603. Thedistance between parent correlithm objects 603 may be referred to as astandard distance. The distance between child correlithm objects 604 andother child correlithm objects 604 or parent correlithm objects 603 maybe referred to as a fractional distance which is less than the standarddistance.

FIG. 7 illustrates another embodiment of how a string correlithm object602 may be implemented within a node 304 by a device 100. In otherembodiments, string correlithm objects 602 may be integrated within asensor 302 or an actor 306. In FIG. 7, a set of correlithm objects 104are shown within an n-dimensional space 102. In one embodiment, thecorrelithm objects 104 are equally spaced from adjacent correlithmobjects 104. A string correlithm object 602 comprises a parentcorrelithm object 603 linked with one or more child correlithm objects604. FIG. 7 illustrates three string correlithm objects 602 where eachstring correlithm object 602 comprises a parent correlithm object 603linked with six child correlithm objects 603. In other examples, then-dimensional space 102 may comprise any suitable number of correlithmobjects 104 and/or string correlithm objects 602.

A parent correlithm object 603 may be a member of one or more stringcorrelithm objects 602. For example, a parent correlithm object 603 maybe linked with one or more sets of child correlithm objects 604 in anode table 200. In one embodiment, a child correlithm object 604 mayonly be linked with one parent correlithm object 603. String correlithmobjects 602 may be configured to form a daisy chain or a linear chain ofchild correlithm objects 604. In one embodiment, string correlithmobjects 602 are configured such that child correlithm objects 604 do notform loops where the chain of child correlithm objects 604 intersectwith themselves. Each child correlithm objects 604 is less than thestandard distance away from its parent correlithm object 603. The childcorrelithm objects 604 are equally spaced from other adjacent childcorrelithm objects 604.

In one embodiment, a data structure such as node table 200 may be usedto map or link parent correlithm objects 603 with child correlithmobjects 604. The node table 200 is generally configured to identify aplurality of parent correlithm objects 603 and one or more childcorrelithm objects 604 linked with each of the parent correlithm objects603. For example, node table 200 may be configured with a first columnthat lists child correlithm objects 604 and a second column that listsparent correlithm objects 603. In other examples, the node table 200 maybe configured in any other suitable manner or may be implemented usingany other suitable data structure. In some embodiments, one or moremapping functions may be used to convert between a child correlithmobject 604 and a parent correlithm object 603.

FIG. 8 is a schematic diagram of another embodiment of a device 100implementing string correlithm objects 602 in a node 304 for acorrelithm object processing system 300. Previously in FIG. 7, a stringcorrelithm object 602 comprised of child correlithm objects 604 that areadjacent to a parent correlithm object 603. In FIG. 8, string correlithmobjects 602 comprise one or more child correlithm objects 604 in betweena pair of parent correlithm objects 603. In this configuration, thestring correlithm object 602 initially diverges from a first parentcorrelithm object 603A and then later converges toward a second parentcorrelithm object 603B. This configuration allows the correlithm objectprocessing system 300 to generate a string correlithm object 602 betweena particular pair of parent correlithm objects 603.

The string correlithm objects described in FIG. 8 allow the device 100to interpolate value between a specific pair of correlithm objects 104(i.e. parent correlithm objects 603). In other words, these types ofstring correlithm objects 602 allow the device 100 to performinterpolation between a set of parent correlithm objects 603.Interpolation between a set of parent correlithm objects 603 enables thedevice 100 to perform operations such as quantization which convertbetween different orders of correlithm objects 104.

In one embodiment, a data structure such as node table 200 may be usedto map or link the parent correlithm objects 603 with their respectivechild correlithm objects 604. For example, node table 200 may beconfigured with a first column that lists child correlithm objects 604and a second column that lists parent correlithm objects 603. In thisexample, a first portion of the child correlithm objects 604 is linkedwith the first parent correlithm object 603A and a second portion of thechild correlithm objects 604 is linked with the second parent correlithmobject 603B. In other examples, the node table 200 may be configured inany other suitable manner or may be implemented using any other suitabledata structure. In some embodiments, one or more mapping functions maybe used to convert between a child correlithm object 604 and a parentcorrelithm object 603.

FIG. 9 is an embodiment of a graph of a probability distribution 900 formatching a random correlithm object 104 with a particular correlithmobject 104. Axis 902 indicates the number of bits that are differentbetween a random correlithm object 104 with a particular correlithmobject 104. Axis 904 indicates the probability associated with aparticular number of bits being different between a random correlithmobject 104 and a particular correlithm object 104.

As an example, FIG. 9 illustrates the probability distribution 900 formatching correlithm objects 104 in a 64-dimensional space 102. In oneembodiment, the probability distribution 900 is approximately a Gaussiandistribution. As the number of dimensions in the n-dimensional space 102increases, the probability distribution 900 starts to shape more like animpulse response function. In other examples, the probabilitydistribution 900 may follow any other suitable type of distribution.

Location 906 illustrates an exact match between a random correlithmobject 104 with a particular correlithm object 104. As shown by theprobability distribution 900, the probability of an exact match betweena random correlithm object 104 with a particular correlithm object 104is extremely low. In other words, when an exact match occurs the eventis most likely deliberate and not a random occurrence.

Location 908 illustrates when all of the bits between the randomcorrelithm object 104 with the particular correlithm object 104 aredifferent. In this example, the random correlithm object 104 and theparticular correlithm object 104 have 64 bits that are different fromeach other. As shown by the probability distribution 900, theprobability of all the bits being different between the randomcorrelithm object 104 and the particular correlithm object 104 is alsoextremely low.

Location 910 illustrates an average number of bits that are differentbetween a random correlithm object 104 and the particular correlithmobject 104. In general, the average number of different bits between therandom correlithm object 104 and the particular correlithm object 104 isequal to n/2 (also referred to as standard distance), where ‘n’ is thenumber of dimensions in the n-dimensional space 102. In this example,the average number of bits that are different between a randomcorrelithm object 104 and the particular correlithm object 104 is 32bits.

Location 912 illustrates a cutoff region that defines a core distancefor a correlithm object core. The correlithm object 104 at location 906may also be referred to as a root correlithm object for a correlithmobject core. The core distance defines the maximum number of bits thatcan be different between a correlithm object 104 and the root correlithmobject to be considered within a correlithm object core for the rootcorrelithm object. In other words, the core distance defines the maximumnumber of hops away a correlithm object 104 can be from a rootcorrelithm object to be considered a part of the correlithm object corefor the root correlithm object. Additional information about acorrelithm object core is described in FIG. 10. In this example, thecutoff region defines a core distance equal to six standard deviationsaway from the average number of bits that are different between a randomcorrelithm object 104 and the particular correlithm object 104. Ingeneral, the standard deviation is equal to √{square root over (n/4)},where ‘n’ is the number of dimensions in the n-dimensional space 102. Inthis example, the standard deviation of the 64-dimensional space 102 isequal to 4 bits. This means the cutoff region (location 912) is located24 bits away from location 910 which is 8 bits away from the rootcorrelithm object at location 906. In other words, the core distance isequal to 8 bits. This means that the cutoff region at location 912indicates that the core distance for a correlithm object core includescorrelithm objects 104 that have up to 8 bits different then the rootcorrelithm object or are up to 8 hops away from the root correlithmobject. In other examples, the cutoff region that defines the coredistance may be equal any other suitable value. For instance, the cutoffregion may be set to 2, 4, 8, 10, 12, or any other suitable number ofstandard deviations away from location 910.

FIG. 10 is a schematic diagram of an embodiment of a device 100implementing a correlithm object core 1002 in a node 304 for acorrelithm object processing system 300. In other embodiments,correlithm object cores 1002 may be integrated with a sensor 302 or anactor 306. Correlithm object cores 1002 can be used by a correlithmobject processing system 300 to classify or group correlithm objects 104and/or the data samples they represent. For example, a set of correlithmobjects 104 can be grouped together by linking them with a correlithmobject core 1402. The correlithm object core 1002 identifies the classor type associated with the set of correlithm objects 104.

In one embodiment, a correlithm object core 1002 comprises a rootcorrelithm object 1004 that is linked with a set of correlithm objects104. The set of correlithm objects 104 that are linked with the rootcorrelithm object 1004 are the correlithm objects 104 which are locatedwithin the core distance of the root correlithm object 1004. The set ofcorrelithm objects 104 are linked with only one root correlithm object1004. The core distance can be computed using a process similar to theprocess described in FIG. 9. For example, in a 64-dimensional space 102with a core distance defined at six sigma (i.e. six standarddeviations), the core distance is equal to 8-bits. This means thatcorrelithm objects 104 within up to eight hops away from the rootcorrelithm object 1004 are members of the correlithm object core 1002for the root correlithm object 1004.

In one embodiment, a data structure such as node table 200 may be usedto map or link root correlithm objects 1004 with sets of correlithmobjects 104. The node table 200 is generally configured to identify aplurality of root correlithm objects 1004 and correlithm objects 104linked with the root correlithm objects 1004. For example, node table200 may be configured with a first column that lists correlithm objectcores 1002, a second column that lists root correlithm objects 1004, anda third column that lists correlithm objects 104. In other examples, thenode table 200 may be configured in any other suitable manner or may beimplemented using any other suitable data structure. In someembodiments, one or more mapping functions may be used to convertbetween correlithm objects 104 and a root correlithm object 1004.

FIG. 11 is an embodiment of a graph of probability distributions 1100for adjacent root correlithm objects 1004. Axis 1102 indicates thedistance between the root correlithm objects 1004, for example, in unitsof bits. Axis 1104 indicates the probability associated with the numberof bits being different between a random correlithm object 104 and aroot correlithm object 1004.

As an example, FIG. 11 illustrates the probability distributions foradjacent root correlithm objects 1004 in a 1024-dimensional space 102.Location 1106 illustrates the location of a first root correlithm object1004 with respect to a second root correlithm object 1004. Location 1108illustrates the location of the second root correlithm object 1004. Eachroot correlithm object 1004 is located an average distance away fromeach other which is equal to n/2, where ‘n’ is the number of dimensionsin the n-dimensional space 102. In this example, the first rootcorrelithm object 1004 and the second root correlithm object 1004 are512 bits or 32 standard deviations away from each other.

In this example, the cutoff region for each root correlithm object 1004is located at six standard deviations from locations 1106 and 1108. Inother examples, the cutoff region may be located at any other suitablelocation. For example, the cutoff region defining the core distance mayone, two, four, ten, or any other suitable number of standard deviationsaway from the average distance between correlithm objects 104 in then-dimensional space 102. Location 1110 illustrates a first cutoff regionthat defines a first core distance 1114 for the first root correlithmobject 1004. Location 1112 illustrates a second cutoff region thatdefines a second core distance 1116 for the second root correlithmobject 1004.

In this example, the core distances for the first root correlithm object1004 and the second root correlithm object 1004 do not overlap with eachother. This means that correlithm objects 104 within the correlithmobject core 1002 of one of the root correlithm objects 1004 are uniquelyassociated with the root correlithm object 1004 and there is noambiguity.

FIG. 12A illustrates one embodiment of a string correlithm objectgenerator 1200 configured to generate a string correlithm object 602 asoutput. String correlithm object generator 1200 is implemented by stringcorrelithm object engine 522 and comprises a first processing stage 1202a communicatively and logically coupled to a second processing stage1202 b. First processing stage 1202 receives an input 1204 and outputs afirst sub-string correlithm object 1206 a that comprises an n-bitdigital word wherein each bit has either a value of zero or one. In oneembodiment, first processing stage 1202 generates the values of each bitrandomly. Input 1204 comprises one or more parameters used to determinethe characteristics of the string correlithm object 602. For example,input 1204 may include a parameter for the number of dimensions, n, inthe n-dimensional space 102 (e.g., 64, 128, 256, etc.) in which togenerate the string correlithm object 602. Input 1204 may also include adistance parameter, δ, that indicates a particular number of bits of then-bit digital word (e.g., 4, 8, 16, etc.) that will be changed from onesub-string correlithm object 1206 to the next in the string correlithmobject 602. Second processing stage 1202 b receives the first sub-stringcorrelithm object 1206 a and, for each bit of the first sub-stringcorrelithm object 1206 a up to the particular number of bits identifiedin the distance parameter, δ, changes the value from a zero to a one orfrom a one to a zero to generate a second sub-string correlithm object1206 b. The bits of the first sub-string correlithm object 1206 a thatare changed in value for the second sub-string correlithm object 1206 bare selected randomly from the n-bit digital word. The other bits of then-bit digital word in second sub-string correlithm object 1206 b remainthe same values as the corresponding bits of the first sub-stringcorrelithm object 1206 a.

FIG. 12B illustrates a table 1220 that demonstrates the changes in bitvalues from a first sub-string correlithm object 1206 a to a secondsub-string correlithm object 1206 b. In this example, assume that n=64such that each sub-string correlithm object 1206 of the stringcorrelithm object 602 is a 64-bit digital word. As discussed previouslywith regard to FIG. 9, the standard deviation is equal to √{square rootover (n/4)}, or four bits, for a 64-dimensional space 102. In oneembodiment, the distance parameter, δ, is selected to equal the standarddeviation. In this embodiment, the distance parameter is also four bitswhich means that four bits will be changed from each sub-stringcorrelithm object 1206 to the next in the string correlithm object 602.In other embodiments where it is desired to create a tighter correlationamong sub-string correlithm objects 1206, a distance parameter may beselected to be less than the standard deviation (e.g., distanceparameter of three bits or less where standard deviation is four bits).In still other embodiments where it is desired to create a loosercorrelation among sub-string correlithm objects 1206, a distanceparameter may be selected to be more than the standard deviation (e.g.,distance parameter of five bits or more where standard deviation is fourbits). Table 1220 illustrates the first sub-string correlithm object1206 a in the first column having four bit values that are changed, bysecond processing stage 1202 b, from a zero to a one or from a one to azero to generate second sub-string correlithm object 1206 b in thesecond column. By changing four bit values, the core of the firstsub-string correlithm object 1206 a overlaps in 64-dimensional spacewith the core of the second sub-string correlithm object 1206 b.

Referring back to FIG. 12A, the second processing stage 1202 b receivesfrom itself the second sub-string correlithm object 1206 b as feedback.For each bit of the second sub-string correlithm object 1206 b up to theparticular number of bits identified by the distance parameter, thesecond processing stage 1202 b changes the value from a zero to a one orfrom a one to a zero to generate a third sub-string correlithm object1206 c. The bits of the second sub-string correlithm object 1206 b thatare changed in value for the third sub-string correlithm object 1206 care selected randomly from the n-bit digital word. The other bits of then-bit digital word in third sub-string correlithm object 1206 c remainthe same values as the corresponding bits of the second sub-stringcorrelithm object 1206 b. Referring back to table 1220 illustrated inFIG. 12B, the second sub-string correlithm object 1206 b in the secondcolumn has four bit values that are changed, by second processing stage1202 b, from a zero to a one or from a one to a zero to generate thirdsub-string correlithm object 1206 c in the third column.

Referring back to FIG. 12A, the second processing stage 1202 bsuccessively outputs a subsequent sub-string correlithm object 1206 bychanging bit values of the immediately prior sub-string correlithmobject 1206 received as feedback, as described above. This processcontinues for a predetermined number of sub-string correlithm objects1206 in the string correlithm object 602. Together, the sub-stringcorrelithm objects 1206 form a string correlithm object 602 in which thefirst sub-string correlithm object 1206 a precedes and is adjacent tothe second sub-string correlithm object 1206 b, the second sub-stringcorrelithm object 1206 b precedes and is adjacent to the thirdsub-string correlithm object 1206 c, and so on. Each sub-stringcorrelithm object 1206 is separated from an adjacent sub-stringcorrelithm object 1206 in n-dimensional space 102 by a number of bitsrepresented by the distance parameter, S.

FIG. 13 is a flowchart of an embodiment of a process 1300 for generatinga string correlithm object 602. At step 1302, a first sub-stringcorrelithm object 1206 a is generated, such as by a first processingstage 1202 a of a string correlithm object generator 1200. The firstsub-string correlithm object 1206 a comprises an n-bit digital word. Atstep 1304, a bit of the n-bit digital word of the sub-string correlithmobject 1206 is randomly selected and is changed at step 1306 from a zeroto a one or from a one to a zero. Execution proceeds to step 1308 whereit is determined whether to change additional bits in the n-bit digitalword. In general, process 1300 will change a particular number of bitsup to the distance parameter, S. In one embodiment, as described abovewith regard to FIGS. 12A-B, the distance parameter is four bits. Ifadditional bits remain to be changed in the sub-string correlithm object1206, then execution returns to step 1304. If all of the bits up to theparticular number of bits in the distance parameter have already beenchanged, as determined at step 1308, then execution proceeds to step1310 where the second sub-string correlithm object 1206 b is output. Theother bits of the n-bit digital word in second sub-string correlithmobject 1206 b remain the same values as the corresponding bits of thefirst sub-string correlithm object 1206 a.

Execution proceeds to step 1312 where it is determined whether togenerate additional sub-string correlithm objects 1206 in the stringcorrelithm object 602. If so, execution returns back to step 1304 andthe remainder of the process occurs again to change particular bits upto the number of bits in the distance parameter, S. Each subsequentsub-string correlithm object 1206 is separated from the immediatelypreceding sub-string correlithm object 1206 in n-dimensional space 102by a number of bits represented by the distance parameter, S. If no moresub-string correlithm objects 1206 are to be generated in the stringcorrelithm object 602, as determined at step 1312, execution of process1300 terminates at steps 1314.

A string correlithm object 602 comprising a series of adjacentsub-string correlithm objects 1206 whose cores overlap with each otherpermits data values to be correlated with each other in n-dimensionalspace 102. Thus, where discrete data values have a pre-existingrelationship with each other in the real-world, those relationships canbe maintained in n-dimensional space 102 if they are represented bysub-string correlithm objects of a string correlithm object 602. Forexample, the letters of an alphabet have a relationship with each otherin the real-world. In particular, the letter “A” precedes the letters“B” and “C” but is closer to the letter “B” than the letter “C”. Thus,if the letters of an alphabet are to be represented by a stringcorrelithm object 602, the relationship between letter “A” and theletters “B” and “C” should be maintained such that “A” precedes but iscloser to letter “B” than letter “C.” Similarly, the letter “B” isequidistant to both letters “A” and “C,” but the letter “B” issubsequent to the letter “A” and preceding the letter “C”. Thus, if theletters of an alphabet are to be represented by a string correlithmobject 602, the relationship between letter “B” and the letters “A” and“C” should be maintained such that the letter “B” is equidistant butsubsequent to letter “A” and preceding letter “C.” The ability tomigrate these relationships between data values in the real-world torelationships among correlithm objects provides a significant advance inthe ability to record, store, and faithfully reproduce data withindifferent computing environments.

FIG. 14 illustrates how data values that have pre-existing relationshipswith each other can be mapped to sub-string correlithm objects 1206 of astring correlithm object 602 in n-dimensional space 102 by stringcorrelithm object engine 522 to maintain their relationships to eachother. Although the following description of FIG. 14 is illustrated withrespect to letters of an alphabet as representing data values that havepre-existing relationships to each other, other data values can also bemapped to string correlithm objects 602 using the techniques discussedherein. In particular, FIG. 14 illustrates a node table 1400 stored inmemory 504 that includes a column for a subset of sub-string correlithmobjects 1206 of a string correlithm object 602. The first sub-stringcorrelithm object 1206 a is mapped to a discrete data value, such as theletter “A” of the alphabet. The second sub-string correlithm object 1206b is mapped to a discrete data value, such as the letter “B” of thealphabet, and so on with sub-string correlithm objects 1206 c and 1206 dmapped to the letters “C” and “D”. As discussed above, the letters ofthe alphabet have a correlation with each other, including a sequence,an ordering, and a distance from each other. These correlations amongletters of the alphabet could not be maintained as represented inn-dimensional space if each letter was simply mapped to a randomcorrelithm object 104. Accordingly, to maintain these correlations, theletters of the alphabet are mapped to sub-string correlation objects1206 of a string correlation object 602. This is because, as describedabove, the adjacent sub-string correlation objects 1206 of a stringcorrelation object 602 also have a sequence, an ordering, and a distancefrom each other that can be maintained in n-dimensional space.

In particular, just like the letters “A,” “B,” “C,” and “D” have anordered sequence in the real-world, the sub-string correlithm objects1206 a, 1206 b, 1206 c, and 1206 d have an ordered sequence and distancerelationships to each other in n-dimensional space. Similarly, just likethe letter “A” precedes but is closer to the letter “B” than the letter“C” in the real-world, so too does the sub-string correlithm object 1206a precede but is closer to the sub-string correlithm object 1206 b thanthe sub-string correlithm object 1206 c in n-dimensional space.Similarly, just like the letter “B” is equidistant to but in between theletters “A” and “C” in the real world, so too is the sub-stringcorrelithm object 1206 b equidistant to but in between the sub-stringcorrelithm objects 1206 a and 1206 c in n-dimensional space. Althoughthe letters of the alphabet are used to provide an example of data inthe real world that has a sequence, an ordering, and a distancerelationship to each other, one of skill in the art will appreciate thatany data with those characteristics in the real world can be representedby sub-string correlithm objects 1206 to maintain those relationships inn-dimensional space.

Because the sub-string correlithm objects 1206 of a string correlithmobject 602 maintains the sequence, ordering, and/or distancerelationships between real-world data in n-dimensional space, node 304can output the real-world data values (e.g., letters of the alphabet) inthe sequence in which they occurred. In one embodiment, the sub-stringcorrelithm objects 1206 can also be associated with timestamps, t₁₋₄, toaid with maintaining the relationship of the real-world data with asequence using the time at which they occurred. For example, sub-stringcorrelithm object 1206 a can be associated with a first timestamp, t₁;sub-string correlithm object 1206 b can be associated with a secondtimestamp, t₂; and so on. In one embodiment where the real-world datarepresents frames of a video signal that occur at different times of anordered sequence, maintaining a timestamp in the node table 1400 aidswith the faithful reproduction of the real-world data at the correcttime in the ordered sequence. In this way, the node table 1400 can actas a recorder by recording discrete data values for a time periodextending from at least the first timestamp, t₁ to a later timestamp,t_(n). Also, in this way, the node 304 is also configured to reproduceor playback the real-world data represented by the sub-string correlithmobjects 1206 in the node table 1400 for a period of time extending fromat least the first timestamp, t₁ to a later timestamp, t_(n). Theability to record real-world data, associate it to sub-string correlithmobjects 1206 in n-dimensional space while maintaining its order,sequence, and distance relationships, and subsequently faithfullyreproduce the real-world data as originally recorded provides asignificant technical advantage to computing systems.

The examples described above relate to representing discrete datavalues, such as letters of an alphabet, using sub-string correlithmobjects 1206 of a string correlithm object 602. However, sub-stringcorrelithm objects 1206 also provide the flexibility to representnon-discrete data values, or analog data values, using interpolationfrom the real world to n-dimensional space 102. FIG. 15A illustrates howanalog data values that have pre-existing relationships with each othercan be mapped to sub-string correlithm objects 1206 of a stringcorrelithm object 602 in n-dimensional space 102 by string correlithmobject engine 522 to maintain their relationships to each other. FIG.15A illustrates a node table 1500 stored in memory 504 that includes acolumn for each sub-string correlithm object 1206 of a string correlithmobject 602. The first sub-string correlithm object 1206 a is mapped toan analog data value, such as the number “1.0”. The second sub-stringcorrelithm object 1206 b is mapped to an analog data value, such as thenumber “2.0”, and so on with sub-string correlithm objects 1206 c and1206 d mapped to the numbers “3.0” and “4.0.” Just like the letters ofthe alphabet described above, these numbers have a correlation with eachother, including a sequence, an ordering, and a distance from eachother. One difference between representing discrete data values (e.g.,letters of an alphabet) and analog data values (e.g., numbers) usingsub-string correlithm objects 1206 is that new analog data values thatfall between pre-existing analog data values can be represented usingnew sub-string correlithm objects 1206 using interpolation, as describedin detail below.

If node 304 receives an input representing an analog data value of 1.5,for example, then string correlithm object engine 522 can determine anew sub-string correlithm object 1206 that maintains the relationshipbetween this input of 1.5 and the other numbers that are alreadyrepresented by sub-string correlithm objects 1206. In particular, nodetable 1500 illustrates that the analog data value 1.0 is represented bysub-string correlithm object 1206 a and analog data value 2.0 isrepresented by sub-string correlithm object 1206 b. Because the analogdata value 1.5 is between the data values of 1.0 and 2.0, then a newsub-string correlithm object 1206 would be created in n-dimensionalspace 102 between sub-string correlithm objects 1206 a and 1206 b. Thisis done by interpolating the distance in n-dimensional space 102 betweensub-string correlithm objects 1206 a and 1206 b that corresponds to thedistance between 1.0 and 2.0 where 1.5 resides and representing thatinterpolation using an appropriate n-bit digital word. In this example,the analog data value of 1.5 is halfway between the data values of 1.0and 2.0. Therefore, the sub-string correlithm object 1206 that isdetermined to represent the analog data value of 1.5 would be halfwaybetween the sub-string correlithm objects 1206 a and 1206 b inn-dimensional space 102. Generating a sub-string correlithm object 1206that is halfway between sub-string correlithm objects 1206 a and 1206 bin n-dimensional space 102 involves modifying bits of the n-bit digitalwords representing the sub-string correlithm objects 1206 a and 1206 b.This process is illustrated with respect to FIG. 15B.

FIG. 15B illustrates a table 1520 with a first column representing then-bit digital word of sub-string correlithm object 1206 a that is mappedin the node table 1500 to the data value 1.0; a second columnrepresenting the n-bit digital word of sub-string correlithm object 1206b that is mapped in the node table 1500 to the data value 2.0; and athird column representing the n-bit digital word of sub-stringcorrelithm object 1206 ab that is generated and associated with the datavalue 1.5. Table 1520 is stored in memory 504. As described above withregard to table 1220, the distance parameter, δ, between adjacentsub-string correlithm objects 1206 a and 1206 b was chosen, in oneembodiment, to be four bits. This means that for a 64-bit digital word,four bits have been changed from a zero to a one or from a one to a zeroin order to generate sub-string correlithm object 1206 b from sub-stringcorrelithm object 1206 a.

In order to generate sub-string correlithm object 1206 ab to representthe data value of 1.5, a particular subset of those four changed bitsbetween sub-string correlithm objects 1206 a and 1206 b should bemodified. Moreover, the actual bits that are changed should be selectedsuccessively from one end of the n-bit digital word or the other end ofthe n-bit digital word. Because the data value of 1.5 is exactly halfwaybetween the data values of 1.0 and 2.0, then it can be determined thatexactly half of the four bits that are different between sub-stringcorrelithm object 1206 a and sub-string correlithm object 1206 b shouldbe changed to generate sub-string correlithm object 1206 ab. In thisparticular example, therefore, starting from one end of the n-bitdigital word as indicated by arrow 1522, the first bit that was changedfrom a value of one in sub-string correlithm object 1206 a to a value ofzero in sub-string correlithm object 1206 b is changed back to a valueof one in sub-string correlithm object 1206 ab. Continuing from the sameend of the n-bit digital word as indicated by arrow 1522, the next bitthat was changed from a value of one in sub-string correlithm object1206 a to a value of zero in sub-string correlithm object 1206 b ischanged back to a value of one in sub-string correlithm object 1206 ab.The other two of the four bits that were changed from sub-stringcorrelithm object 1206 a to sub-string correlithm object 1206 b are notchanged back. Accordingly, two of the four bits that were differentbetween sub-string correlithm objects 1206 a and 1206 b are changed backto the bit values that were in sub-string correlithm object 1206 a inorder to generate sub-string correlithm object 1206 ab that is halfwaybetween sub-string correlithm objects 1206 a and 1206 b in n-dimensionalspace 102 just like data value 1.5 is halfway between data values 1.0and 2.0 in the real world.

Other input data values can also be interpolated and represented inn-dimensional space 102, as described above. For example, if the inputdata value received was 1.25, then it is determined to be one-quarter ofthe distance from the data value 1.0 and three-quarters of the distancefrom the data value 2.0. Accordingly, a sub-string correlithm object1206 ab can be generated by changing back three of the four bits thatdiffer between sub-string correlithm objects 1206 a and 1206 b. In thisregard, the sub-string correlithm object 1206 ab (which represents thedata value 1.25) will only differ by one bit from the sub-stringcorrelithm object 1206 a (which represents the data value 1.0) inn-dimensional space 102. Similarly, if the input data value received was1.75, then it is determined to be three-quarters of the distance fromthe data value 1.0 and one-quarter of the distance from the data value2.0. Accordingly, a sub-string correlithm object 1206 ab can begenerated by changing back one of the four bits that differ betweensub-string correlithm objects 1206 a and 1206 b. In this regard, thesub-string correlithm object 1206 ab (which represents the data value1.75) will differ by one bit from the sub-string correlithm object 1206b (which represents the data value 2.0) in n-dimensional space 102. Inthis way, the distance between data values in the real world can beinterpolated to the distance between sub-string correlithm objects 1206in n-dimensional space 102 in order to preserve the relationships amonganalog data values.

Although the example above was detailed with respect to changing bitvalues from the top end of the n-bit digital word represented by arrow1522, the bit values can also be successively changed from the bottomend of the n-bit digital word. The key is that of the bit values thatdiffer from sub-string correlithm object 1206 a to sub-string correlithmobject 1206 b, the bit values that are changed to generate sub-stringcorrelithm object 1206 ab should be taken consecutively as they areencountered whether from the top end of the n-bit digital word (asrepresented by arrow 1522) or from the bottom end of the n-bit digitalword. This ensures that sub-string correlithm object 1206 ab willactually be between sub-string correlithm objects 1206 a and 1206 brather than randomly drifting away from both of sub-string correlithmobjects 1206 a and 1206 b in n-dimensional space 102.

FIG. 16 illustrates how real-world data values can be aggregated andrepresented by correlithm objects 104 (also referred to as non-stringcorrelithm objects 104), which are then linked to correspondingsub-string correlithm objects 1206 of a string correlithm object 602 bystring correlithm object engine 522. As described above with regard toFIG. 12A, a string correlithm object generator 1200 generates sub-stringcorrelithm objects 1206 that are adjacent to each other in n-dimensionalspace 102 to form a string correlithm object 602. The sub-stringcorrelithm objects 1206 a-n embody an ordering, sequence, and distancerelationships to each other in n-dimensional space 102. As described indetail below, non-string correlithm objects 104 can be mapped tocorresponding sub-string correlithm objects 1206 and stored in a nodetable 1600 to provide an ordering or sequence among them inn-dimensional space 102. This allows node table 1600 to record, store,and faithfully reproduce or playback a sequence of events that arerepresented by non-string correlithm objects 104 a-n. In one embodiment,the sub-string correlithm objects 1206 and the non-string correlithmobjects 104 can both be represented by the same length of digital word,n, (e.g., 64 bit, 128 bit, 256 bit). In another embodiment, thesub-string correlithm objects 1206 can be represented by a digital wordof one length, n, and the non-string correlithm objects 104 can berepresented by a digital word of a different length, m.

In a particular embodiment, the non-string correlithm objects 104 a-ncan represent aggregated real-world data. For example, real-world datamay be generated related to the operation of an automated teller machine(ATM). In this example, the ATM machine may have a video camera and amicrophone to tape both the video and audio portions of the operation ofthe ATM by one or more customers in a vestibule of a bank facility ordrive-through. The ATM machine may also have a processor that conductsand stores information regarding any transactions between the ATM andthe customer associated with a particular account. The bank facility maysimultaneously record video, audio, and transactional aspects of theoperation of the ATM by the customer for security, audit, or otherpurposes. By aggregating the real-world data values into non-stringcorrelithm objects 104 and associating those non-string correlithmobjects 104 with sub-string correlithm objects 1206, as described ingreater detail below, the correlithm object processing system maymaintain the ordering, sequence, and other relationships between thereal-world data values in n-dimensional space 102 for subsequentreproduction or playback. Although the example above is detailed withrespect to three particular types of real-world data (i.e., video,audio, transactional data associated with a bank ATM) that areaggregated and represented by correlithm objects 104, it should beunderstood that any suitable number and combination of different typesof real-world data can be aggregated and represented in this example.

For a period of time from t₁ to t_(n), the ATM records video, audio, andtransactional real-world data. For example, the period of time mayrepresent an hour, a day, a week, a month, or other suitable time periodof recording. The real-world video data is represented by videocorrelithm objects 1602. The real-world audio data is represented byaudio correlithm objects 1604. The real-world transaction data isrepresented by transaction correlithm objects 1606. The correlithmobjects 1602, 1604, and 1606 can be aggregated to form non-stringcorrelithm objects 104. For example, at a first time, t₁, the ATMgenerates: (a) real-world video data that is represented as a firstvideo correlithm object 1602 a; (b) real-world audio data that isrepresented by a first audio correlithm object 1604 a; and (c)real-world transaction data that is represented by a first transactioncorrelithm object 1606 a. Correlithm objects 1602 a, 1604 a, and 1606 acan be represented as a single non-string correlithm object 104 a whichis then associated with first sub-string correlithm object 1206 a in thenode table 1600. In one embodiment, the timestamp, t₁, can also becaptured in the non-string correlithm object 104 a. In this way, threedifferent types of real-world data are captured, represented by anon-string correlithm object 104 and then associated with a portion ofthe string correlithm object 602.

Continuing with the example, at a second time, t₂, the ATM generates:(a) real-world video data that is represented as a second videocorrelithm object 1602 b; (b) real-world audio data that is representedby a second audio correlithm object 1604 b; and (c) real-worldtransaction data that is represented by a second transaction correlithmobject 1606 b. The second time, t₂, can be a predetermined time orsuitable time interval after the first time, t₁, or it can be at a timesubsequent to the first time, t₁, where it is determined that one ormore of the video, audio, or transaction data has changed in anmeaningful way (e.g., video data indicates that a new customer enteredthe vestibule of the bank facility; another audible voice is detected orthe customer has made an audible request to the ATM; or the customer isattempting a different transaction or a different part of the sametransaction). Correlithm objects 1602 b, 1604 b, and 1606 b can berepresented as a single non-string correlithm object 104 b which is thenassociated with second sub-string correlithm object 1206 b in the nodetable 1600. In one embodiment, the timestamp, t₂, can also be capturedin the non-string correlithm object 104 b.

Continuing with the example, at a third time, t₃, the ATM generates: (a)real-world video data that is represented as a third video correlithmobject 1602 c; (b) real-world audio data that is represented by a thirdaudio correlithm object 1604 c; and (c) real-world transaction data thatis represented by a third transaction correlithm object 1606 c. Thethird time, t₃, can be a predetermined time or suitable time intervalafter the second time, t₂, or it can be at a time subsequent to thesecond time, t₂, where it is determined that one or more of the video,audio, or transaction data has changed again in a meaningful way, asdescribed above. Correlithm objects 1602 c, 1604 c, and 1606 c can berepresented as a single non-string correlithm object 104 c which is thenassociated with third sub-string correlithm object 1206 c in the nodetable 1600. In one embodiment, the timestamp, t₃, can also be capturedin the non-string correlithm object 104 c.

Concluding with the example, at an n-th time, t_(n), the ATM generates:(a) real-world video data that is represented as an n-th videocorrelithm object 1602 n; (b) real-world audio data that is representedby an n-th audio correlithm object 1604 n; and (c) real-worldtransaction data that is represented by an n-th transaction correlithmobject 1606 n. The third time, t_(n), can be a predetermined time orsuitable time interval after a previous time, t_(n-1), or it can be at atime subsequent to the previous time, t_(n-1), where it is determinedthat one or more of the video, audio, or transaction data has changedagain in a meaningful way, as described above. Correlithm objects 1602n, 1604 n, and 1606 n can be represented as a single non-stringcorrelithm object 104 n which is then associated with n-th sub-stringcorrelithm object 1206 n in the node table 1600. In one embodiment, thetimestamp, t_(n), can also be captured in the non-string correlithmobject 104 n.

As illustrated in FIG. 16, different types of real-world data (e.g.,video, audio, transactional) can be captured and represented bycorrelithm objects 1602, 1604, and 1606 at particular timestamps. Thosecorrelithm objects 1602, 1604, and 1606 can be aggregated intocorrelithm objects 104. In this way, the real-world data can be “fannedin” and represented by a common correlithm object 104. By capturingreal-world video, audio, and transaction data at different relevanttimestamps from t₁-t_(n), representing that data in correlithm objects104, and then associating those correlithm objects 104 with sub-stringcorrelithm objects 1206 of a string correlithm object 602, the nodetable 1600 described herein can store vast amounts of real-world data inn-dimensional space 102 for a period of time while preserving theordering, sequence, and relationships among real-world data events andcorresponding correlithm objects 104 so that they can be faithfullyreproduced or played back in the real-world, as desired. This provides asignificant savings in memory capacity.

FIG. 17 is a flowchart of an embodiment of a process 1700 for linkingnon-string correlithm objects 104 with sub-string correlithm objects1206. At step 1702, string correlithm object generator 1200 generates afirst sub-string correlithm object 1206 a. Execution proceeds to step1704 where correlithm objects 104 are used to represent different typesof real-world data at a first timestamp, t₁. For example, correlithmobject 1602 a represents real-world video data; correlithm object 1604 arepresents real-world audio data; and correlithm object 1606 arepresents real-world transaction data. At step 1706, each of correlithmobjects 1602 a, 1604 a, and 1606 a captured at the first timestamp, t₁,are aggregated and represented by a non-string correlithm object 104 a.Execution proceeds to step 1708, where non-string correlithm object 104a is linked to sub-string correlithm object 1206 a, and this associationis stored in node table 1600 at step 1710. At step 1712, it isdetermined whether real-world data at the next timestamp should becaptured. For example, if a predetermined time interval since the lasttimestamp has passed or if a meaningful change to the real-world datahas occurred since the last timestamp, then execution returns to steps1702-1710 where another sub-string correlithm object 1206 is generated(step 1702); correlithm objects representing real-world data is capturedat the next timestamp (step 1704); those correlithm objects areaggregated and represented in a non-string correlithm object 104 (step1706); that non-string correlithm object 104 is linked with a sub-stringcorrelithm object 1206 (step 1708); and this association is stored inthe node table 1600 (step 1710). If no further real-world data is to becaptured at the next timestamp, as determined at step 1712, thenexecution ends at step 1714.

FIG. 18 illustrates how sub-string correlithm objects 1206 a-e of afirst string correlithm object 602 a are linked to sub-string correlithmobjects 1206 x-z of a second string correlithm object 602 b by stringcorrelithm object engine 522. The first string correlithm object 602 aincludes sub-string correlithm objects 1206 a-e that are separated fromeach other by a first distance 1802 in n-dimensional space 102. Thesecond string correlithm object 602 b includes sub-string correlithmobjects 1206 x-z that are separated from each other by a second distance1804 in n-dimensional space 102. In one embodiment, the sub-stringcorrelithm objects 1206 a-e of the first string correlithm object 602 aand the sub-string correlithm objects 1206 x-z can both be representedby the same length of digital word, n, (e.g., 64-bit, 128-bit, 256-bit).In another embodiment, the sub-string correlithm objects 1206 a-e of thefirst string correlithm object 602 a can be represented by a digitalword of one length, n, and the sub-string correlithm objects 1206 x-z ofthe second string correlithm object 602 b can be represented by adigital word of a different length, m. Each sub-string correlithm object1206 a-e represents a particular data value, such as a particular typeof real-world data value. When a particular sub-string correlithm object1206 a-e of the first string correlithm object 602 is mapped to aparticular sub-string correlithm object 1206 x-z of the second stringcorrelithm object 602, as described below, then the data valueassociated with the sub-string correlithm object 1206 a-e of the firststring correlithm object 602 a becomes associated with the mappedsub-string correlithm object 1206 x-z of the second string correlithmobject 602 b.

Mapping data represented by sub-string correlithm objects 1206 a-e of afirst string correlithm object 602 a in a smaller n-dimensional space102 (e.g., 64-bit digital word) where the sub-string correlithm objects1206 a-e are more tightly correlated to sub-string correlithm objects1206 x-z of a second string correlithm object 602 b in a largern-dimensional space 102 (e.g., 256-bit digital word) where thesub-string correlithm objects 1206 x-y are more loosely correlated (orvice versa) can provide several technical advantages in a correlithmobject processing system. For example, such a mapping can be used tocompress data and thereby save memory resources. In another example,such a mapping can be used to spread out data and thereby createadditional space in n-dimensions for the interpolation of data. In yetanother example, such a mapping can be used to apply a transformationfunction to the data (e.g., linear transformation function or non-lineartransformation function) from the first string correlithm object 602 ato the second string correlithm object 602 b.

The mapping of a first string correlithm object 602 a to a secondcorrelithm object 602 b operates, as described below. First, a node 304receives a particular sub-string correlithm object 1206, such as 1206 billustrated in FIG. 18. To map this particular sub-string correlithmobject 1206 b to the second correlithm object 602 b, the node 304determines the proximity of it to corresponding sub-string correlithmobjects 1206 x and 1206 y in second string correlithm object 602 b(e.g., by determining the Hamming distance between 1206 b and 1206 x,and between 1206 b and 1206 y). In particular, node 304 determines afirst proximity 1806 in n-dimensional space between the sub-stringcorrelithm object 1206 b and sub-string correlithm object 1206 x; anddetermines a second proximity 1808 in n-dimensional space between thesub-string correlithm object 1206 b and sub-string correlithm object1206 y. As illustrated in FIG. 18, the first proximity 1806 is smallerthan the second proximity 1808. Therefore, sub-string correlithm object1206 b is closer in n-dimensional space 102 to sub-string correlithmobject 1206 x than to sub-string correlithm object 1206 y. Accordingly,node 304 maps sub-string correlithm object 1206 b of first stringcorrelithm object 602 a to sub-string correlithm object 1206 x of secondstring correlithm object 602 b and maps this association in node table1820 stored in memory 504.

The mapping of the first string correlithm object 602 a to a secondcorrelithm object 602 b continues in operation, as described below. Thenode 304 receives another particular sub-string correlithm object 1206,such as 1206 c illustrated in FIG. 18. To map this particular sub-stringcorrelithm object 1206 c to the second correlithm object 602 b, the node304 determines the proximity of it to corresponding sub-stringcorrelithm objects 1206 x and 1206 y in second string correlithm object602 b. In particular, node 304 determines a first proximity 1810 inn-dimensional space between the sub-string correlithm object 1206 c andsub-string correlithm object 1206 x; and determines a second proximity1812 in n-dimensional space between the sub-string correlithm object1206 c and sub-string correlithm object 1206 y. As illustrated in FIG.18, the second proximity 1812 is smaller than the second proximity 1810.Therefore, sub-string correlithm object 1206 c is closer inn-dimensional space 102 to sub-string correlithm object 1206 y than tosub-string correlithm object 1206 x. Accordingly, node 304 mapssub-string correlithm object 1206 c of first string correlithm object602 a to sub-string correlithm object 1206 y of second string correlithmobject 602 b and maps this association in node table 1820.

The sub-string correlithm objects 1206 a-e may be associated withtimestamps in order to capture a temporal relationship among them andwith the mapping to sub-string correlithm objects 1206 x-z. For example,sub-string correlithm object 1206 a may be associated with a firsttimestamp, second sub-string correlithm object 1206 b may be associatedwith a second timestamp later than the first timestamp, and so on.

FIG. 19 is a flowchart of an embodiment of a process 1900 for linking afirst string correlithm object 602 a with a second string correlithmobject 602 b. At step 1902, a first string correlithm object 602 a isreceived at node 304. The first correlithm object 602 a includes a firstplurality of sub-string correlithm objects 1206, such as 1206 a-eillustrated in FIG. 18. Each of these sub-string correlithm objects 1206a-e are separated from each other by a first distance 1802 inn-dimensional space 102. At step 1904, a second string correlithm object602 b is received at node 304. The second correlithm object 602 bincludes a second plurality of sub-string correlithm objects 1206, suchas 1206 x-z illustrated in FIG. 18. Each of these sub-string correlithmobjects 1206 x-z are separated from each other by a second distance 1804in n-dimensional space 102. At step 1906, node 304 receives a particularsub-string correlithm object 1206 of the first string correlithm object602 a. At step 1908, node 304 determines a first proximity inn-dimensional space 102, such as proximity 1806 illustrated in FIG. 18,to a corresponding sub-string correlithm object 1206 of secondcorrelithm object 602 b, such as sub-string correlithm object 1206 xillustrated in FIG. 18. At step 1910, node 304 determines a secondproximity in n-dimensional space 102, such as proximity 1808 illustratedin FIG. 18, to a corresponding sub-string correlithm object 1206 ofsecond correlithm object 602 b, such as sub-string correlithm object1206 y illustrated in FIG. 18.

At step 1912, node 304 selects the sub-string correlithm object 1206 ofsecond string correlithm object 602 b to which the particular sub-stringcorrelithm object 1206 received at step 1906 is closest in n-dimensionalspace based upon the first proximity determined at step 1908 and thesecond proximity determined at step 1910. For example, as illustrated inFIG. 18, sub-string correlithm object 1206 b is closer in n-dimensionalspace to sub-string correlithm object 1206 x than sub-string correlithmobject 1206 y based on first proximity 1806 being smaller than secondproximity 1808. Execution proceeds to step 1914 where node 304 maps theparticular sub-string correlithm object 1206 received at step 1906 tothe sub-string correlithm object 1206 of second string correlithm object602 b selected at step 1912. At step 1916, node 304 determines whetherthere are any additional sub-string correlithm objects 1206 of firststring correlithm object 602 a to map to the second string correlithmobject 602 b. If so, execution returns to perform steps 1906 through1914 with respect to a different particular sub-string correlithm object1206 of first string correlithm object 602 a. If not, executionterminates at step 1918.

FIG. 20 illustrates one embodiment of an actor 306 that operates usingan actor table 310 that maps sub-string correlithm objects 1206 a-d of astring correlithm object 602 in n-dimensional space 102 to analog ordiscrete data values. Actor 306 may be implemented by actor engine 514,as described above with respect to FIG. 5. Although the followingdescription of FIG. 20 is illustrated with respect to analog data values(e.g. numbers 1.0, 2.0, 3.0, 4.0, etc.) that have a pre-existingrelationship to each other, other analog or discrete data values canalso be mapped to sub-string correlithm objects 1206 a-d in actor table310, as described below. In particular, FIG. 20 illustrates an actortable 310 stored in memory 504 that includes a row for a subset ofsub-string correlithm objects 1206 of string correlithm object 602. Thefirst sub-string correlithm object 1206 a is mapped to an analog datavalue, such as the number 1.0. The second sub-string correlithm object1206 b is mapped to an analog data value, such as the number 2.0, and soon with sub-string correlithm objects 1206 c and 1206 d mapped to thenumbers 3.0 and 4.0, respectively. The analog data values 1.0, 2.0, 3.0,4.0, etc. have a correlation with each other, including a sequence, anordering, and a distance from each other. To maintain thesecorrelations, these analog data values are mapped to sub-stringcorrelithm objects 1206 of a string correlithm object 602 in actor table310. This is because, as described above, the adjacent sub-stringcorrelation objects 1206 of a string correlation object 602 also have asequence, an ordering, and a distance from each other that can bemaintained in n-dimensional space 102. The sub-string correlithm objects1206 of string correlithm object 602 described herein are particularembodiments of correlithm objects 104 described above.

In particular, just like the analog data values 1.0, 2.0, 3.0, and 4.0have an ordered sequence as real-world data values 326, the sub-stringcorrelithm objects 1206 a, 1206 b, 1206 c, and 1206 d have an orderedsequence and distance relationship to each other in n-dimensional space102. For example, just like the analog data value 1.0 precedes but iscloser to 2.0 than 3.0, so too does the sub-string correlithm object1206 a precede but is closer to the sub-string correlithm object 1206 bthan the sub-string correlithm object 1206 c in n-dimensional space 102.Similarly, just like the analog data value 2.0 is equidistant to but inbetween 1.0 and 3.0, so too is the sub-string correlithm object 1206 bequidistant to but in between the sub-string correlithm objects 1206 aand 1206 c in n-dimensional space 102. Although a sequential ordering ofnumbers is used to provide an example of analog data values in the realworld that has a sequence, an ordering, and a distance relationship toeach other, one of skill in the art will appreciate that any data withthose characteristics in the real world can be represented by sub-stringcorrelithm objects 1206 to maintain those relationships in n-dimensionalspace 102. For example, actor table 310 may map the sub-stringcorrelithm objects 1206 a-d to an ordered sequence of letters in thealphabet, such as letters “A,” “B,” “C,” and “D”. In another example,actor table 310 may map the sub-string correlithm objects 1206 a-d to anordered sequence of digital data values, such as the binary digits “1,”“0,” “0,” “1”.

Actor 306 serves as an interface that allows a user device 100 toconvert correlithm objects 104 in the correlithm object domain back toreal world values 326 or data samples. Actor 306 enables the user device100 to convert from correlithm objects 104 into any suitable type ofreal world value. Actor 306 is configured to receive a correlithm object104 (e.g. an output correlithm object 104 from a node 304), to determinea real-world output value 326 based on the received correlithm object104, and to output the real-world output value 326. In particular, actor306 receives an input correlithm object 104 and compares it with thesub-string correlithm objects 1206 to identify the particular sub-stringcorrelithm object 1206 that is closest in n-dimensional space 102 toinput correlithm object 104. For example, node 304 determines thedistances in n-dimensional space 102 between input correlithm object 104and each of the sub-string correlithm objects 1206. In one embodiment,these distances may be determined by calculating Hamming distancesbetween input correlithm object 104 and each of the sub-stringcorrelithm objects 1206. In another embodiment, these distances may bedetermined by calculating the anti-Hamming distances between inputcorrelithm object 104 and each of the sub-string correlithm objects1206.

The Hamming distance may be determined based on the number of bits thatdiffer between the binary string representing input correlithm object104 and each of the binary strings representing each of the sub-stringcorrelithm objects 1206 a-d. The anti-Hamming distance may be determinedbased on the number of bits that are the same between the binary stringrepresenting input correlithm object 104 and each of the binary stringsrepresenting each of the sub-string correlithm objects 1206 a-d. Instill other embodiments, the distances in n-dimensional space betweeninput correlithm object 104 and each of the correlithm objects 1206 a-dmay be determined using a Minkowski distance or a Euclidean distance.

Upon calculating the n-dimensional distances between input correlithmobject 104 and the sub-string correlithm objects 1206 a-d using one ofthe techniques described above, actor 306 determines which calculatedn-dimensional distance is the shortest. This is because the sub-stringcorrelithm object 1206 having the shortest n-dimensional distancebetween it and input correlithm object 104 received by actor 306 can bethought of as being the most statistically similar match. Actor 306identifies the data value that corresponds to the sub-string correlithmobject 1206 that was determined to have the shortest n-dimensionaldistance between it and input correlithm object 104, and outputs thisdata value as real-world data value 326. For example, if actor 306determined that sub-string correlithm object 1206 c had the shortestn-dimensional distance between it and input correlithm object 104, actor306 would output the value 3.0 as the real-world data value 326.

In a particular embodiment, actor 306 does not necessarily calculate then-dimensional distance between the input correlithm object 104 and eachsub-string correlithm object 1206 stored in actor table 310. Instead,actor 306 can take advantage of the fact that the sub-string correlithmobjects 1206 a-d follow an ordered sequence to determine when furthercomparisons of input correlithm object 104 with sub-string correlithmobjects 1206 are no longer needed in order to find the sub-stringcorrelithm object 1206 with the shortest n-dimensional distance. Anexample operation will be described to illustrate this concept. Assumethat actor 306 receives an input correlithm object 104. Actor 306compares the input correlithm object 104 with the first sub-stringcorrelithm object 1206 stored in actor table 310, which in this exampleis sub-string correlithm object 1206 a. Assume that actor 306 determinesa first Hamming distance for this comparison. Next, actor 306 comparesthe input correlithm object 104 with the second sub-string correlithmobject 1206 stored in actor table 310, which in this example issub-string correlithm object 1206 b. Assume that actor 306 determines asecond Hamming distance which is smaller than the first Hammingdistance, indicating that second sub-string correlithm object 1206 b hasa shorter n-dimensional distance between it and input correlithm object104 than first sub-string correlithm object 1206 a. Next, actor 306compares the input correlithm object 104 with the third sub-stringcorrelithm object 1206 c. Assume that actor 306 determines a thirdHamming distance which is larger than the second Hamming distance,indicating that third sub-string correlithm object 1206 c has a largern-dimensional distance between it and input correlithm object 104 thansecond sub-string correlithm object 1206 b. At this point, actor 306 canconclude that because sub-string correlithm objects 1206 follow anordered sequence, any further comparisons of input correlithm object 104with sub-string correlithm objects 1206 will only produce n-dimensionaldistances that are larger than the second Hamming distance. In otherwords, actor 306 determined an inflection point in the determination ofn-dimensional distances when it proceeded from second sub-stringcorrelithm object 1206 b to third sub-string correlithm object 1206 c(i.e., the second n-dimensional distance was shorter than the firstn-dimensional distance, but the third n-dimensional distance was largerthan the second n-dimensional distance, thereby indicating an inflectionpoint). Accordingly, actor 306 determines that the second sub-stringcorrelithm object 1206 b has the shortest n-dimensional distance betweenit and input correlithm object 104. In other words, second sub-stringcorrelithm object 1206 b is the most statistically similar match toinput correlithm object 104. In this embodiment, there was no need todetermine the n-dimensional distance between input correlithm object 104and fourth sub-string correlithm object 1206 d. Thus, actor 306 did notperform any further calculations of n-dimensional distances beyond thethird n-dimensional distance calculation in this example, and therebysaved time, memory, and processing resources.

Although this particular example was detailed with respect to startingby comparing the input correlithm object 104 and the first sub-stringcorrelithm object 1206 a, and then calculating n-dimensional distanceswith input correlithm object 104 sequentially through the remainder ofthe sub-string correlithm objects 1206, it should be understood thatactor 306 could start by comparing input correlithm object 104 and thelast sub-string correlithm object 1206 d and then calculatingn-dimensional distances with input correlithm object 104 sequentially inthe opposite direction through the remainder of the sub-stringcorrelithm objects 1206 c-a. In still other examples, actor 306 couldstart by comparing input correlithm object 104 with the sub-stringcorrelithm objects 1206 at either end of the actor table 310 and thencalculating n-dimensional distances with input correlithm object 104sequentially toward the middle of the sub-string correlithm objects 1206until the n-dimensional distance determination between input correlithmobject 104 and any particular sub-string correlithm object 1206 reversesthe trend of n-dimensional distances getting smaller (e.g., determinedHamming distances between input correlithm object 104 and eachsuccessive sub-string correlithm object 1206 that were getting smallersuddenly get larger, that is, hit an inflection point) or getting larger(e.g., determined Hamming distances between input correlithm object 104and each successive sub-string correlithm object 1206 that were gettinglarger suddenly get smaller, that is, hit an inflection point). In stillanother example, actor 306 could start by comparing input correlithmobject 104 with the sub-string correlithm object 1206 at or near themiddle of the list of sub-string correlithm objects 1206 in actor table310 and then calculating n-dimensional distances with input correlithmobject 104 sequentially outward in both directions until then-dimensional distance determination between input correlithm object 104and any particular sub-string correlithm object 1206 reverses the trendof n-dimensional distances getting smaller (e.g., determined Hammingdistances between input correlithm object 104 and each successivesub-string correlithm object 1206 that were getting smaller suddenly getlarger, that is, hit an inflection point) or getting larger (e.g.,determined Hamming distances between input correlithm object 104 andeach successive sub-string correlithm object 1206 that were gettinglarger suddenly get smaller, that is, hit an inflection point).

In a particular embodiment, actor 306 may use only a subset of the bitsof a binary string that forms the input correlithm object 104 and thebinary strings that form the sub-string correlithm objects 1206 toperform the n-dimensional distance calculation. For example, if theinput correlithm object 104 and the sub-string correlithm objects 1206each comprise 256-bit binary strings, then actor 306 may compare only aparticular subset of bits of input correlithm object 104 (e.g., thefirst 64 bits) with a corresponding subset of bits of the sub-stringcorrelithm objects 1206 (e.g., the first 64 bits) to determinen-dimensional distances and identify the shortest n-dimensionaldistance, as described above. This embodiment allows a 64-bit processorto more readily perform the operations involved with determiningn-dimensional distances, and thereby saves time, memory, and processingresources while still identifying a statistically significant result.

FIG. 21 is a flowchart of an embodiment of a process 2100 for comparingan input correlithm object 104 with sub-string correlithm objects 1206in an actor table 310, identifying the sub-string correlithm object 1206with the smallest n-dimensional distance to the input correlithm object104 and outputting a real-world data value 326 corresponding to theidentified sub-string correlithm object 1206. At step 2102, actor 306stores an actor table 310 that includes a plurality of sub-stringcorrelithm objects 1206 and corresponding real-world data values 326.Actor 306 receives input correlithm object 104 at step 2104. At step2106, actor 306 determines a first n-dimensional distance between inputcorrelithm object 104 and a first sub-string correlithm object 1206 inactor table 310. Execution proceeds to step 2108 where actor 306determines a second n-dimensional distance between input correlithmobject 104 and a second sub-string correlithm object 1206 that isadjacent to the first sub-string correlithm object 1206 used in step2106. Execution proceeds to step 2110, where actor 306 determineswhether the second n-dimensional distance determined at step 2108 issmaller or larger than the first n-dimensional distance determined atstep 2106.

If the second n-dimensional distance is larger than the firstn-dimensional distance, execution proceeds to step 2112 where actor 306determines a third n-dimensional distance between input correlithmobject 104 and a third sub-string correlithm object 1206 adjacent to thesecond sub-string correlithm object 1206. At step 2114, actor 306determines that the third n-dimensional distance determined at step 2112is smaller than the second n-dimensional distance determined at step2108. Accordingly, at step 2120, actor 306 determines that secondsub-string correlithm object 1206 has the smallest n-dimensionaldistance to input correlithm object 104. Actor 306 outputs thereal-world data value 326 associated with second sub-string correlithmobject 1206 at step 2122.

If the second n-dimensional distance is smaller than the firstn-dimensional distance, as determined at step 2110, execution proceedsto step 2116 where actor 306 determines a third n-dimensional distancebetween input correlithm object 104 and a third sub-string correlithmobject 1206 adjacent to the second sub-string correlithm object 1206. Atstep 2118, actor 306 determines that the third n-dimensional distancedetermined at step 2112 is larger than the second n-dimensional distancedetermined at step 2108. Accordingly, at step 2120, actor 306 determinesthat second sub-string correlithm object 1206 has the smallestn-dimensional distance to input correlithm object 104. Actor 306 outputsthe real-world data value 326 associated with second sub-stringcorrelithm object 1206 at step 2122. Execution terminates at step 2124.

FIG. 22 illustrates one embodiment of a correlithm object processingsystem 2200, including an integrator node engine 2201 that receives aninput correlithm object 2202 and a feedback correlithm object 2204, andoutputs an output correlithm object 2206 using an integrator value table2208 (illustrated in FIG. 23) stored in memory 504. The integrator nodeengine 2201 emulates an integration circuit in correlithm objectprocessing system 2200. The output of integrator node engine 2201 is theintegration of the values represented by the input correlithm objects2202 with respect to time. For example, where the input correlithmobjects 2202 represent voltage values, the output of the integrator nodeengine 2201 is the integration of the voltage values over time. Each ofinput correlithm objects 2202, feedback correlithm objects 2204, andoutput correlithm objects 2206 comprise correlithm objects 104 asillustrated and described, for example, with respect to FIGS. 1 to 5. Inparticular, each of input correlithm objects 2202, feedback correlithmobjects 2204, and output correlithm objects 2206 comprises n-bit digitalwords of binary values, as described, for example, with respect to atleast FIGS. 1-5.

In a particular embodiment, integrator node engine 2201 receives astring of input correlithm objects 2202 that are arranged in a stringcorrelithm object 2210 and outputs a string of output correlithm objects2206 that are arranged in a string correlithm object 2212. For example,the string correlithm object 2210 comprises a first sub-stringcorrelithm object 2210 a that corresponds to a first input correlithmobject 2202 received at a first time, a second sub-string correlithmobject 2210 b that corresponds to a second input correlithm object 2202received at a second time, and a third sub-string correlithm object 2210c that corresponds to a third input correlithm object 2202 received at athird time. In this embodiment, the string correlithm object 2212comprises a first sub-string correlithm object 2212 a that correspondsto a first output correlithm object 2206, a second sub-string correlithmobject 2212 b that corresponds to a second output correlithm object2206, a third sub-string correlithm object 2212 c that corresponds to athird output correlithm object 2206, and a fourth sub-string correlithmobject 2212 d that corresponds to a fourth output correlithm object2206. The first sub-string correlithm object 2212 a represents aninitial output value of the integrator node engine 2200 at the outset ofoperation. In this embodiment, the initial output value comprises zero.The second sub-string correlithm object 2212 b represents theintegration of the string correlithm object 2210 over a first timeperiod. The third sub-string correlithm object 2212 c represents theintegration of the string correlithm object 2210 over a second timeperiod. The fourth sub-string correlithm object 2212 d represents theintegration of the string correlithm object 2210 over a third timeperiod.

Referring to FIG. 23, one embodiment of an integrator value table 2208is illustrated with columns and rows. The columns of table 2208 compriseinput values represented by first correlithm objects 2214. The rows oftable 2208 comprise feedback values represented by second correlithmobjects 2216. The intersections of input values and feedback valuescomprise output values represented by third correlithm objects 2218. Forease of understanding, the notation used to identify a correlithm objectindicates the value that it represents (e.g., C₁ used to represent value1; C₂ used to represent value 2; and so forth). The first correlithmobjects 2214, second correlithm objects 2216, and third correlithmobjects 2218 comprise correlithm objects 104 as illustrated anddescribed, for example, with respect to FIGS. 1-5. Each output valuerepresented by a third correlithm object 2218 in integrator value table2208 is the sum of a corresponding input value and a correspondingfeedback value. For example, the intersection of first correlithm object2214 (C₁) and second correlithm object 2216 (C₀) is a third correlithmobject 2218 (C₁) (e.g., 1+0=1). In another example, the intersection offirst correlithm object 2214 (C₀) and second correlithm object 2216 (C₁)is a third correlithm object 2218 (C₁) (e.g., 0+1=1). In still anotherexample, the intersection of first correlithm object 2214 (C₁) andsecond correlithm object 2216 (C₁) is a third correlithm object 2218(C₂) (e.g., 1+1=2). Referring back to FIG. 22, the integrator nodeengine 2201 uses integrator value table 2208 to determine outputcorrelithm object 2206 based on input correlithm object 2202 andfeedback correlithm object 2204, as described below.

An example operation of integrator node engine 2201 is described withrespect to input correlithm objects 2202 arranged in string correlithmobject 2210 and output correlithm objects 2206 arranged in stringcorrelithm object 2212. Note that correlithm object processing system2200 may operate with input correlithm objects 2202 and outputcorrelithm objects 2206 that are not organized as string correlithmobjects. In this example, integrator node engine 2201 initially outputsan output correlithm object 2206 that represents a zero value. Thisoutput correlithm object 2206 is the first sub-string correlithm object2212 a of string correlithm object 2212. Node 2201 then receives a firstinput correlithm object 2202 (e.g., C₁ representing the value “1”) assub-string correlithm object 2210 a at a first time. At the outset ofoperation, node engine 2201 receives a feedback correlithm object 2204(e.g., C₀ representing the value “0”) that corresponds to the initialoutput correlithm object 2206 (e.g., zero value). The first inputcorrelithm object 2202 (e.g., C₁) is identified in the second column oftable 2208 illustrated in FIG. 23, and first feedback correlithm object2204 (e.g., C₀) is identified in first row of table 2208 illustrated inFIG. 23. Node 2201 determines an output correlithm object 2218 (e.g.,C₁) based on the intersection of first input correlithm object 2204(e.g., C₁) and first feedback correlithm object 2204 (e.g., C₀) in table2208. The output correlithm object 2218 (e.g., C₁) is the secondsub-string correlithm object 2212 b of string correlithm object 2212,which represents the integration of the input correlithm objects 2202over a first time period.

Node 2201 then receives a second input correlithm object 2202 (e.g., C₀representing the value “0”) as sub-string correlithm object 2210 b at asecond time. At this point of operation, node engine 2201 receives afeedback correlithm object 2204 (e.g., C₁ representing the value “1”)that corresponds to the output correlithm object 2206 (e.g., “1”) atthat time. The second input correlithm object 2202 (e.g., C₀) isidentified in the first column of table 2208 illustrated in FIG. 23, andthe feedback correlithm object 2204 (e.g., C₁) is identified in thesecond row of table 2208 illustrated in FIG. 23. Node 2201 determines anoutput correlithm object 2218 (e.g., C₁) based on the intersection ofsecond input correlithm object 2204 (e.g., C₀) and feedback correlithmobject 2204 (e.g., C₁) in table 2208. The output correlithm object 2218(e.g., C₁) is the third sub-string correlithm object 2212 c of stringcorrelithm object 2212, which represents the integration of the inputcorrelithm objects 2202 over a second time period.

Node 2201 then receives a third input correlithm object 2202 (e.g., C₁representing the value “1”) as sub-string correlithm object 2210 c at athird time. At this point of operation, node engine 2201 receives afeedback correlithm object 2204 (e.g., C₁ representing the value “1”)that corresponds to the output correlithm object 2206 (e.g., “1”) atthat time. The third input correlithm object 2202 (e.g., C₁) isidentified in the second column of table 2208 illustrated in FIG. 23,and the feedback correlithm object 2204 (e.g., C₁) is identified in thesecond row of table 2208 illustrated in FIG. 23. Node 2201 determines anoutput correlithm object 2218 (e.g., C₂ based on the intersection ofthird input correlithm object 2204 (e.g., C₁) and feedback correlithmobject 2204 (e.g., C₁) in table 2208. The output correlithm object 2218(e.g., C₂) is the fourth sub-string correlithm object 2212 d of stringcorrelithm object 2212, which represents the integration of the inputcorrelithm objects 2202 over a third time period.

In a particular embodiment, node engine 2201 determines the Hammingdistance (or otherwise determines the distance in n-dimensional space)between input correlithm object 2202 and the first correlithm objects2214 of integrator value table 2208, and between the feedback correlithmobject 2204 and the second correlithm objects 2216 of integrator valuetable 2208. Even if noise modifies some of the bits of the n-bit inputcorrelithm object 2202 and/or some of the bits of the n-bit correlithmobjects 2214 representing the input values in table 2208, the smallestdetermined Hamming distance between the input correlithm object 2202 andthe first correlithm objects 2214 will identify the appropriate entryfor table 2208. Similarly, node engine 2201 determines the Hammingdistance (or otherwise determines the distance in n-dimensional space)between feedback correlithm object 2204 and the second correlithmobjects 2216 of integrator value table 2208. Even if noise modifies someof the bits of the n-bit feedback correlithm object 2204 and/or some ofthe bits of the n-bit correlithm objects 2216 representing the feedbackvalues in table 2208, the smallest determined Hamming distance betweenthe feedback correlithm object 2204 and the second correlithm objects2216 will identify the appropriate entry for table 2208.

FIG. 24 illustrates one embodiment of a correlithm object processingsystem 2400, including a differentiator node engine 2401 that receivesan input correlithm object 2402, stores a previous value correlithmobject 2404, and outputs an output correlithm object 2406 using adifferentiator value table 2408 (illustrated in FIG. 25) stored inmemory 504. The differentiator node engine 2401 emulates adifferentiator circuit in correlithm object processing system 2400. Theoutput of differentiator node engine 2401 is the differentiation of thevalues represented by the input correlithm objects 2402 with respect totime. For example, where the input correlithm objects 2402 representvoltage values, the output of the differentiator node engine 2401 is thedifferentiation of the voltage values over time. Each of inputcorrelithm objects 2402, previous value correlithm objects 2404, andoutput correlithm objects 2406 comprise correlithm objects 104 asillustrated and described, for example, with respect to FIGS. 1 to 5. Inparticular, each of input correlithm objects 2402, previous valuecorrelithm objects 2404, and output correlithm objects 2406 comprisen-bit digital words of binary values, as described, for example, withrespect to at least FIGS. 1-5.

In a particular embodiment, differentiator node engine 2401 receives aseries of input correlithm objects 2402 that are arranged in a stringcorrelithm object 2410 and outputs a series of output correlithm objects2406 that are arranged in a string correlithm object 2412. For example,in one embodiment, the string correlithm object 2410 comprises a firstsub-string correlithm object 2410 a that corresponds to a first inputcorrelithm object 2402 received at a first time, a second sub-stringcorrelithm object 2410 b that corresponds to a second input correlithmobject 2402 received at a second time, a third sub-string correlithmobject 2410 b that corresponds to a third input correlithm object 2402received at a third time, a fourth sub-string correlithm object 2410 dthat corresponds to a fourth input correlithm object 2402 received at afourth time, and a fifth sub-string correlithm object 2410 e thatcorresponds to a fifth input correlithm object 2402 received at a fifthtime.

The previous value correlithm objects 2404 are associated with the inputcorrelithm objects 2402. In particular, when differentiator node engine2401 receives second sub-string correlithm object 2410 b (e.g., “1”)after previously receiving first sub-string correlithm object 2410 a(e.g., “0”), then previous value correlithm object 2404 a has a valuethat is equal to the first sub-string correlithm object 2410 a (e.g.,“0”) because it was the “previous” value of the input correlithm object2402. Similarly, when differentiator node engine 2401 receives thirdsub-string correlithm object 2410 c (e.g., “2”) after previouslyreceiving second sub-string correlithm object 2410 b (e.g., “1”), thenprevious value correlithm object 2404 b has a value that is equal to thesecond sub-string correlithm object 2410 b (e.g., “1”) because it wasthe “previous” value of the input correlithm object 2402. Furthermore,when differentiator node engine 2401 receives fourth sub-stringcorrelithm object 2410 d (e.g., “1”) after previously receiving thirdsub-string correlithm object 2410 c (e.g., “2”), then previous valuecorrelithm object 2404 c has a value that is equal to the thirdsub-string correlithm object 2410 c (e.g., “2”) because it was the“previous” value of the input correlithm object 2402. Finally, whendifferentiator node engine 2401 receives fifth sub-string correlithmobject 2410 e (e.g., “0”) after previously receiving fourth sub-stringcorrelithm object 2410 d (e.g., “1”), then previous value correlithmobject 2404 d has a value that is equal to the fourth sub-stringcorrelithm object 2410 d (e.g., “1”) because it was the “previous” valueof the input correlithm object 2402.

In this embodiment, the string correlithm object 2412 comprises a firstsub-string correlithm object 2412 a that corresponds to a first outputcorrelithm object 2406, a second sub-string correlithm object 2412 bthat corresponds to a second output correlithm object 2406, a thirdsub-string correlithm object 2412 c that corresponds to a third outputcorrelithm object 2406, a fourth sub-string correlithm object 2412 dthat corresponds to a fourth output correlithm object 2406, and a fifthsub-string correlithm object 2412 e that corresponds to a fifth outputcorrelithm object 2406. The second sub-string correlithm object 2412 brepresents the differentiation of the string correlithm object 2410 overa first time period. The third sub-string correlithm object 2412 crepresents the differentiation of the string correlithm object 2410 overa second time period. The fourth sub-string correlithm object 2412 drepresents the differentiation of the string correlithm object 2410 overa third time period. The fifth sub-string correlithm object 2412 erepresents the differentiation of the string correlithm object 2410 overa fourth time period.

Referring to FIG. 25, one embodiment of a differentiator value table2408 is illustrated with columns and rows. The columns of table 2408comprise input values represented by first correlithm objects 2414. Therows of table 2408 comprise previous values represented by secondcorrelithm objects 2416. The intersections of input values and previousvalues comprise output values represented by third correlithm objects2418. For ease of understanding, the notation used to identify acorrelithm object indicates the value that it represents (e.g., C₁ usedto represent value 1; C₂ used to represent value 2; and so forth). Thefirst correlithm objects 2414, second correlithm objects 2416, and thirdcorrelithm objects 2418 comprise correlithm objects 104 as illustratedand described, for example, with respect to FIGS. 1-5. Each output valuerepresented by a third correlithm object 2418 in differentiator valuetable 2408 is the difference of a corresponding input value and acorresponding previous value. For example, the intersection of firstcorrelithm object 2414 (C₁) and second correlithm object 2416 (C₀) is athird correlithm object 2418 (C₁) (e.g., 1-0=1). In another example, theintersection of first correlithm object 2414 (C₂) and second correlithmobject 2416 (C₁) is a third correlithm object 2418 (C₁) (e.g., 2-1=1).In still another example, the intersection of first correlithm object2414 (C₁) and second correlithm object 2416 (C₂) is a third correlithmobject 2418 (C₁) (e.g., 1-2=−1). Referring back to FIG. 24, thedifferentiator node engine 2401 uses differentiator value table 2408 todetermine output correlithm object 2406 based on input correlithm object2402 and previous value correlithm object 2404, as described below.

An example operation of differentiator node engine 2401 is describedwith respect to input correlithm object 2402 arranged in stringcorrelithm object 2410, previous value correlithm objects 2404 a-d, andoutput correlithm objects 2406 arranged in string correlithm object2412. Note that correlithm object processing system 2400 may operatewith input correlithm objects 2202 and output correlithm objects 2206that are not organized as string correlithm objects. In this example,integrator node engine 2401 initially outputs an output correlithmobject 2406 that represents a zero value. This output correlithm object2406 is the first sub-string correlithm object 2412 a of stringcorrelithm object 2412 and simply represents the value of the firstsub-string correlithm object 2410 a.

Node 2401 then receives a second input correlithm object 2402 as secondsub-string correlithm object 2410 b (e.g., C₁ representing the value“1”). Node 2401 stores a first previous value correlithm object 2404 athat represents the value of the first sub-string correlithm object 2410a (e.g., C₀ representing the value “0”). The second input correlithmobject 2402 (e.g., C₁) is identified in the second column of table 2408illustrated in FIG. 25, and first previous value correlithm object 2404a (e.g., C₀) is identified in third row of table 2408 illustrated inFIG. 25. Node 2401 determines an output correlithm object 2418 (e.g.,C₁) based on the intersection of first input correlithm object 2404(e.g., C₁) and first previous value correlithm object 2404 a (e.g., C₀)in table 2408. The output correlithm object 2418 (e.g., C₁) is thesecond sub-string correlithm object 2412 b of string correlithm object2412, which represents the differentiation of the input correlithmobjects 2402 over a first time period. In this example, the secondsub-string correlithm object 2412 b represents a value of “1” which isthe difference between the value of the second sub-string correlithmobject 2410 b (e.g., “1”) and the first previous value correlithm object2404 a (e.g., “0”).

Node 2401 then receives a third input correlithm object 2402 as thirdsub-string correlithm object 2410 c (e.g., C₂ representing the value“2”). Node 2401 stores a second previous value correlithm object 2404 bthat represents the value of the second sub-string correlithm object2410 b (e.g., C₁ representing the value “1”). The third input correlithmobject 2402 (e.g., C₂) is identified in the third column of table 2408illustrated in FIG. 25, and the second previous correlithm object 2404 b(e.g., C₁) is identified in the fourth row of table 2408 illustrated inFIG. 25. Node 2401 determines an output correlithm object 2418 (e.g.,C₁) based on the intersection of third input correlithm object 2404(e.g., C₂) and second previous value correlithm object 2404 b (e.g., C₁)in table 2408. The output correlithm object 2418 (e.g., C₁) is the thirdsub-string correlithm object 2412 c of string correlithm object 2412,which represents the differentiation of the input correlithm objects2402 over a second time period. In this example, the third sub-stringcorrelithm object 2412 c represents a value of “1” which is thedifference between the value of the third sub-string correlithm object2410 c (e.g., “2”) and the second previous value correlithm object 2404b (e.g., “1”).

Node 2401 then receives a fourth input correlithm object 2402 as fourthsub-string correlithm object 2410 d (e.g., C₁ representing the value“1”). Node 2401 stores a third previous value correlithm object 2404 cthat represents the value of the third sub-string correlithm object 2410c (e.g., C₂ representing the value “2”). The fourth input correlithmobject 2402 (e.g., C₁) is identified in the second column of table 2408illustrated in FIG. 25, and the third previous correlithm object 2404 c(e.g., C₂) is identified in the fifth row of table 2408 illustrated inFIG. 25. Node 2401 determines an output correlithm object 2418 (e.g.,C⁻¹) based on the intersection of fourth input correlithm object 2404(e.g., C₁) and third previous value correlithm object 2404 c (e.g., C₂)in table 2408. The output correlithm object 2418 (e.g., C⁻¹) is thefourth sub-string correlithm object 2412 d of string correlithm object2412, which represents the differentiation of the input correlithmobjects 2402 over a third time period. In this example, the fourthsub-string correlithm object 2412 d represents a value of “−1” which isthe difference between the value of the fourth sub-string correlithmobject 2410 d (e.g., “1”) and the third previous value correlithm object2404 c (e.g., “2”).

Node 2401 then receives a fifth input correlithm object 2402 as fifthsub-string correlithm object 2410 e (e.g., C₀ representing the value“0”). Node 2401 stores a fourth previous value correlithm object 2404 dthat represents the value of the fourth sub-string correlithm object2410 d (e.g., C₁ representing the value “1”). The fifth input correlithmobject 2402 (e.g., C₀) is identified in the first column of table 2408illustrated in FIG. 25, and the fourth previous correlithm object 2404 d(e.g., C₁) is identified in the fourth row of table 2408 illustrated inFIG. 25. Node 2401 determines an output correlithm object 2418 (e.g.,C₁) based on the intersection of fifth input correlithm object 2404(e.g., C₀) and fourth previous value correlithm object 2404 d (e.g., C₁)in table 2408. The output correlithm object 2418 (e.g., C₁) is the fifthsub-string correlithm object 2412 e of string correlithm object 2412,which represents the differentiation of the input correlithm objects2402 over a fourth time period. In this example, the fifth sub-stringcorrelithm object 2412 e represents a value of “−1” which is thedifference between the value of the fifth sub-string correlithm object2410 e (e.g., “0”) and the fourth previous value correlithm object 2404d (e.g., “1”).

In a particular embodiment, node engine 2401 determines the Hammingdistance (or otherwise determines the distance in n-dimensional space)between input correlithm object 2402 and the first correlithm objects2414 of differentiator value table 2408, and between the previous valuecorrelithm object 2404 and the second correlithm objects 2416 ofdifferentiator value table 2408. Even if noise modifies some of the bitsof the n-bit input correlithm object 2402 and/or some of the bits of then-bit correlithm objects 2414 representing the input values in table2408, the smallest determined Hamming distance between the inputcorrelithm object 2402 and the first correlithm objects 2414 willidentify the appropriate entry for table 2408. Similarly, node engine2401 determines the Hamming distance (or otherwise determines thedistance in n-dimensional space) between previous value correlithmobject 2404 and the second correlithm objects 2416 of differentiatorvalue table 2408. Even if noise modifies some of the bits of the n-bitfeedback correlithm object 2404 and/or some of the bits of the n-bitcorrelithm objects 2416 representing the previous values in table 2408,the smallest determined Hamming distance between the previous valuecorrelithm object 2404 and the second correlithm objects 2416 willidentify the appropriate entry for table 2408.

FIG. 26 illustrates one embodiment of a correlithm object processingsystem 2600, including a differential amplifier node engine 2601 thatreceives a first input correlithm object 2602 and a second inputcorrelithm object 2604, identifies an amplifier value correlithm object2605, and outputs an output correlithm object 2606 using a differentialvalue table 2608 (illustrated in FIG. 27A) and an amplifier value table2610 (illustrated in FIG. 27B) stored in memory 504. The differentialamplifier node engine 2601 emulates a differential operational amplifiercircuit in correlithm object processing system 2600. The output ofdifferential amplifier node engine 2601 is the amplified differencebetween the values represented by the first input correlithm object 2602and the second input correlithm object 2604. For example, where thefirst input correlithm object 2602 represents a first voltage, V₁, thesecond input correlithm object 2604 represents a second voltage, V₂, andthe amplifier value correlithm object 2605 represents an amplificationvalue, A, then the output correlithm object 2606 represents an outputvoltage, V_(out), according to the following relationship:V_(out)=A*(V₁-V₂). Each of first input correlithm object 2602, secondinput correlithm object 2604, amplifier value correlithm object 2605,and output correlithm object 2606 comprise correlithm objects 104 asillustrated and described, for example, with respect to FIGS. 1 to 5. Inparticular, each of first input correlithm object 2602, second inputcorrelithm object 2604, amplifier value correlithm object 2605, andoutput correlithm object 2606 comprise n-bit digital words of binaryvalues, as described, for example, with respect to at least FIGS. 1-5.

Referring to FIG. 27A, one embodiment of a differential value table 2608is illustrated with columns and rows. The columns of table 2608 comprisefirst input values represented by first correlithm objects 2614. Therows of table 2608 comprise second input values represented by secondcorrelithm objects 2616. The intersections of first input values andsecond input values comprise intermediate output values represented bythird correlithm objects 2618. For ease of understanding, the notationused to identify a correlithm object indicates the value that itrepresents (e.g., C₁ used to represent value 1; C₂ used to representvalue 2; and so forth). The first correlithm objects 2614, secondcorrelithm objects 2616, and third correlithm objects 2618 comprisecorrelithm objects 104 as illustrated and described, for example, withrespect to FIGS. 1-5. Each intermediate output value represented by athird correlithm object 2618 in differential value table 2608 is thedifference of a corresponding first input value and a correspondingsecond input value. For example, the intersection of first correlithmobject 2614 (C₁) and second correlithm object 2616 (C₀) is a thirdcorrelithm object 2418 (C₁) (e.g., 1-0=1). In another example, theintersection of first correlithm object 2614 (C₂) and second correlithmobject 2616 (C₀ is a third correlithm object 2618 (C₁) (e.g., 2-1=1). Instill another example, the intersection of first correlithm object 2614(C₁) and second correlithm object 2616 (C₂) is a third correlithm object2618 (C⁻¹) (e.g., 1-2=−1). Referring back to FIG. 26, the differentialamplifier node engine 2601 uses differential value table 2608 inconjunction with amplifier value table 2610 described below, todetermine output correlithm object 2606 based on first input correlithmobject 2602, second input correlithm object 2604, and amplifier valuecorrelithm object 2605.

Referring to FIG. 27B, one embodiment of amplifier value table 2610 isillustrated with columns and rows. The columns of table 2610 compriseintermediate output values represented by third correlithm objects 2618.The rows of table 2610 comprise amplifier values represented by fourthcorrelithm objects 2620. The intersections of intermediate output valuesand amplifier values comprise output values represented by fifthcorrelithm objects 2622. For ease of understanding, the notation used toidentify a correlithm object indicates the value that it represents(e.g., C₁ used to represent value 1; C₂ used to represent value 2; andso forth). The third correlithm objects 2618, fourth correlithm objects2620, and fifth correlithm objects 2622 comprise correlithm objects 104as illustrated and described, for example, with respect to FIGS. 1-5.Each output value represented by a fifth correlithm object 2622 inamplifier value table 2610 is the product of a correspondingintermediate output value and a corresponding amplifier value. Forexample, the intersection of third correlithm object 2618 (C₂) andfourth correlithm object 2620 (C₂) is a fifth correlithm object 2622(C₄) (e.g., 2*2=4). In another example, the intersection of thirdcorrelithm object 2618 (C⁻³) and fourth correlithm object 2620 (C₂) is afifth correlithm object 2622 (C⁻⁶) (e.g., −3*2=−6). In still anotherexample, the intersection of third correlithm object 2618 (C₁) andfourth correlithm object 2620 (C₃) is a fifth correlithm object 2622(C₃) (e.g., 1*3=3). Referring back to FIG. 26, the differentialamplifier node engine 2601 uses differential value table 2608 describedabove in conjunction with amplifier value table 2610 to determine outputcorrelithm object 2606 based on first input correlithm object 2602,second input correlithm object 2604, and amplifier value correlithmobject 2605.

An example operation of differential amplifier node engine 2601 isdescribed with respect to example values. In this example, assume thatdifferential amplifier node engine 2601 receives a first inputcorrelithm object 2602 that represents a first input value (e.g., C₂representing the first input value “2”) and further receives a secondcorrelithm object 2604 that represents a second input value (e.g., C₁representing the second input value “1”). Further assume that thisparticular operation of differential amplifier node engine 2601 isassociated with an amplifier value correlithm object 2605 representing aparticular amplifier value (e.g., C₃ representing the amplifier value“3”). In one embodiment, the node engine 2601 receives an amplifiervalue correlithm object 2605 representing a particular amplifier valueas an input. In other embodiments, the particular node engine 2601 maysimply be assigned a particular amplifier value correlithm object 2605representing a particular amplifier value.

Differential amplifier node engine 2601 identifies first inputcorrelithm object 2602 in the third column of table 2608 illustrated inFIG. 27A (e.g., C₂ representing the first input value “2”). Differentialamplifier node engine 2601 identifies second input correlithm object2604 in the second row of table 2608 illustrated in FIG. 27A (e.g., C₁representing the second input value “1”). Node engine 2601 determines athird correlithm object 2618 (e.g., C₁) based on the intersection offirst correlithm object 2614 (e.g., C₂) and second correlithm object2616 (e.g., C₁) in table 2608. Third correlithm object 2618 isassociated with an intermediate output value that represents thedifference between the first input value and the second input value(e.g., 2−1=1).

Differential amplifier node engine 2601 then uses the determined thirdcorrelithm object 2618 from differential value table 2608 in conjunctionwith the amplifier value correlithm object 2605 and the amplifier valuetable 2610 to determine output correlithm object 2606. In particular,differential amplifier node engine 2601 identifies third correlithmobject 2618 in the sixth column of amplifier value table 2610illustrated in FIG. 27B based on the third correlithm object 2618determined from the differential value table 2608 (e.g., C₀. Node engine2601 identifies a fourth correlithm object 2620 in the third row ofamplifier value table 2610 (e.g., C₃) based on amplifier valuecorrelithm object 2605. Node engine 2601 determines a fifth correlithmobject 2622 (e.g., C₃) based on the intersection of third correlithmobject 2618 (e.g., C₁) and fourth correlithm object 2620 (e.g., C₃) intable 2610. Fifth correlithm object 2622 represents the product betweenthe intermediate output value represented by third correlithm object2618 and the amplifier value represented by fourth correlithm object2620 (e.g., 1*3=3). Node engine 2601 outputs the determined fifthcorrelithm object 2622 (e.g., C₃) as output correlithm object 2606,which represents the amplified difference between the first inputcorrelithm object 2602 and the second input correlithm object 2604(e.g., 3=3*(2−1)). In this manner, differential amplifier node engine2601 emulates a differential amplifier circuit in a correlithm objectprocessing system.

In a particular embodiment, node engine 2601 determines the Hammingdistance (or otherwise determines the distance in n-dimensional space)between particular correlithm objects and corresponding entries ofdifferential value table 2608. For example, node engine 2601 determinesthe Hamming distance (or otherwise determines the distance inn-dimensional space) between first input correlithm object 2602 and thefirst correlithm objects 2614 of differential value table 2608, andbetween the second input correlithm object 2604 and the secondcorrelithm objects 2616 of differential value table 2608. Even if noisemodifies some of the bits of the n-bit first input correlithm object2602 and/or some of the bits of the n-bit correlithm objects 2614representing the first input values in table 2608, the smallestdetermined Hamming distance between the first input correlithm object2602 and the first correlithm objects 2614 will identify the appropriateentry for table 2608. Similarly, even if noise modifies some of the bitsof the n-bit second input correlithm object 2604 and/or some of the bitsof the n-bit correlithm objects 2616 representing the second inputvalues in table 2608, the smallest determined Hamming distance betweenthe second input correlithm object 2604 and the second correlithmobjects 2616 will identify the appropriate entry for table 2608.

Relatedly, in one embodiment, node engine 2601 determines the Hammingdistance (or otherwise determines the distance in n-dimensional space)between particular correlithm objects and corresponding entries ofamplifier value table 2610. For example, node engine 2601 determines theHamming distance (or otherwise determines the distance in n-dimensionalspace) between third correlithm object 2618 derived from differentialvalue table 2608 and the third correlithm objects 2618 of amplifiervalue table 2610, and between the amplifier value correlithm object 2605and the fourth correlithm objects 2620 of amplifier value table 2610.Even if noise modifies some of the bits of the n-bit third correlithmobject 2618 derived from differential value table 2608 and/or some ofthe bits of the n-bit correlithm objects 2618 representing theintermediate output values in table 2610, the smallest determinedHamming distance between the third correlithm object 2618 derived fromdifferential value table 2608 and the third correlithm objects 2618 ofamplifier value table 2610 will identify the appropriate entry for table2610. Similarly, even if noise modifies some of the bits of the n-bitamplifier value correlithm object 2605 and/or some of the bits of then-bit correlithm objects 2620 representing the amplifier values in table2610, the smallest determined Hamming distance between the amplifiervalue correlithm object 2605 and the fourth correlithm objects 2620 willidentify the appropriate entry for table 2610.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated in another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

The invention claimed is:
 1. A device configured to emulate adifferential amplifier circuit in a correlithm object processing system,comprising: a memory operable to store a differential value table and anamplifier value table, wherein: the differential value table comprises:a plurality of first input values represented by first correlithmobjects; a plurality of second input values represented by secondcorrelithm objects; and a plurality of intermediate output valuesrepresented by third correlithm objects; wherein each intermediateoutput value in the differential value table is the difference between acorresponding first input value and a corresponding second input value;and the amplifier value table comprises: the plurality of intermediateoutput values represented by the third correlithm objects; a pluralityof amplifier values represented by fourth correlithm objects; and aplurality of output values represented by fifth correlithm objects;wherein each output value in the amplifier table is the product of acorresponding intermediate output value and a corresponding amplifiervalue; and a node engine communicatively coupled to the memory andconfigured to: receive a first input correlithm object representing afirst input value; receive a second input correlithm object representinga second input value; identify an amplifier value correlithm objectrepresenting an amplifier value; determine a first correlithm object inthe differential value table based on the first input correlithm object;determine a second correlithm object in the differential value tablebased on the second input correlithm object; determine a thirdcorrelithm object from the differential value table based on thedetermined first correlithm object and the determined second correlithmobject; determine a fourth correlithm object in the amplifier valuetable based on the determined amplifier value correlithm object;determine an output correlithm object from the amplifier value tablebased on the determined third correlithm object and the determinedfourth correlithm object; and output the output correlithm objectrepresenting an amplified difference between the first input value andthe second input value.
 2. The device of claim 1, wherein each of thecorrelithm objects comprises an n-bit digital word of binary values. 3.The device of claim 1, wherein determining a first correlithm object inthe differential value table based on the first input correlithm objectcomprises: determining Hamming distances between the first inputcorrelithm object and the first correlithm objects in the differentialvalue table; and identifying the first correlithm object in thedifferential value table associated with the smallest determined Hammingdistance between the first input correlithm object and the firstcorrelithm objects in the differential value table.
 4. The device ofclaim 3, wherein determining a second correlithm object in thedifferential value table based on the second input correlithm objectcomprises: determining Hamming distances between the second inputcorrelithm object and the second correlithm objects in the differentialvalue table; and identifying the second correlithm object in thedifferential value table associated with the smallest determined Hammingdistance between the second input correlithm object and the secondcorrelithm objects in the differential value table.
 5. The device ofclaim 4, wherein determining a third correlithm object from thedifferential value table comprises determining a third correlithm objectfrom the differential value table based on the identified firstcorrelithm object and the identified second correlithm object.
 6. Thedevice of claim 5, wherein determining a fourth correlithm object in theamplifier value table based on the determined amplifier value correlithmobject comprises: determining Hamming distances between the amplifiervalue correlithm object and the fourth correlithm objects in theamplifier value table; and identifying the fourth correlithm object inthe amplifier value table associated with the smallest determinedHamming distance between the amplifier value correlithm object and thefourth correlithm objects in the amplifier value table.
 7. The device ofclaim 6, wherein determining an output correlithm object from theamplifier value table comprises determining an output correlithm objectfrom the amplifier value table based on the identified fourth correlithmobject and the determined third correlithm object.
 8. A method foremulating a differential amplifier circuit in a correlithm objectprocessing system, comprising: storing a differential value tablecomprising: a plurality of first input values represented by firstcorrelithm objects; a plurality of second input values represented bysecond correlithm objects; and a plurality of intermediate output valuesrepresented by third correlithm objects; wherein each intermediateoutput value in the differential value table is the difference between acorresponding first input value and a corresponding second input value;storing an amplifier value table comprising: the plurality ofintermediate output values represented by the third correlithm objects;a plurality of amplifier values represented by fourth correlithmobjects; and a plurality of output values represented by fifthcorrelithm objects; wherein each output value in the amplifier table isthe product of a corresponding intermediate output value and acorresponding amplifier value; receiving a first input correlithm objectrepresenting a first input value; receiving a second input correlithmobject representing a second input value; identifying an amplifier valuecorrelithm object representing an amplifier value; determining a firstcorrelithm object in the differential value table based on the firstinput correlithm object; determining a second correlithm object in thedifferential value table based on the second input correlithm object;determining a third correlithm object from the differential value tablebased on the determined first correlithm object and the determinedsecond correlithm object; determining a fourth correlithm object in theamplifier value table based on the determined amplifier value correlithmobject; determining an output correlithm object from the amplifier valuetable based on the determined third correlithm object and the determinedfourth correlithm object; and outputting the output correlithm objectrepresenting an amplified difference between the first input value andthe second input value.
 9. The method of claim 8, wherein each of thecorrelithm objects comprises an n-bit digital word of binary values. 10.The method of claim 8, wherein determining a first correlithm object inthe differential value table based on the first input correlithm objectcomprises: determining Hamming distances between the first inputcorrelithm object and the first correlithm objects in the differentialvalue table; and identifying the first correlithm object in thedifferential value table associated with the smallest determined Hammingdistance between the first input correlithm object and the firstcorrelithm objects in the differential value table.
 11. The method ofclaim 10, wherein determining a second correlithm object in thedifferential value table based on the second input correlithm objectcomprises: determining Hamming distances between the second inputcorrelithm object and the second correlithm objects in the differentialvalue table; and identifying the second correlithm object in thedifferential value table associated with the smallest determined Hammingdistance between the second input correlithm object and the secondcorrelithm objects in the differential value table.
 12. The method ofclaim 11, wherein determining a third correlithm object from thedifferential value table comprises determining a third correlithm objectfrom the differential value table based on the identified firstcorrelithm object and the identified second correlithm object.
 13. Themethod of claim 12, wherein determining a fourth correlithm object inthe amplifier value table based on the determined amplifier valuecorrelithm object comprises: determining Hamming distances between theamplifier value correlithm object and the fourth correlithm objects inthe amplifier value table; and identifying the fourth correlithm objectin the amplifier value table associated with the smallest determinedHamming distance between the amplifier value correlithm object and thefourth correlithm objects in the amplifier value table.
 14. The methodof claim 13, wherein determining an output correlithm object from theamplifier value table comprises determining an output correlithm objectfrom the amplifier value table based on the identified fourth correlithmobject and the determined third correlithm object.
 15. A computerprogram comprising executable instructions stored in a non-transitorycomputer readable medium such that when executed by a processor causesthe processor to emulate a differentiator circuit in a correlithm objectprocessing system configured to: store a differential value tablecomprising: a plurality of first input values represented by firstcorrelithm objects; a plurality of second input values represented bysecond correlithm objects; and a plurality of intermediate output valuesrepresented by third correlithm objects; wherein each intermediateoutput value in the differential value table is the difference between acorresponding first input value and a corresponding second input value;store an amplifier value table comprising: the plurality of intermediateoutput values represented by the third correlithm objects; a pluralityof amplifier values represented by fourth correlithm objects; and aplurality of output values represented by fifth correlithm objects;wherein each output value in the amplifier table is the product of acorresponding intermediate output value and a corresponding amplifiervalue; receive a first input correlithm object representing a firstinput value; receive a second input correlithm object representing asecond input value; identify an amplifier value correlithm objectrepresenting an amplifier value; determine a first correlithm object inthe differential value table based on the first input correlithm object;determine a second correlithm object in the differential value tablebased on the second input correlithm object; determine a thirdcorrelithm object from the differential value table based on thedetermined first correlithm object and the determined second correlithmobject; determine a fourth correlithm object in the amplifier valuetable based on the determined amplifier value correlithm object;determine an output correlithm object from the amplifier value tablebased on the determined third correlithm object and the determinedfourth correlithm object; and output the output correlithm objectrepresenting an amplified difference between the first input value andthe second input value.
 16. The computer program of claim 15, whereineach of the correlithm objects comprises an n-bit digital word of binaryvalues.
 17. The computer program of claim 15, wherein determining afirst correlithm object in the differential value table based on thefirst input correlithm object comprises: determining Hamming distancesbetween the first input correlithm object and the first correlithmobjects in the differential value table; and identifying the firstcorrelithm object in the differential value table associated with thesmallest determined Hamming distance between the first input correlithmobject and the first correlithm objects in the differential value table.18. The computer program of claim 17, wherein determining a secondcorrelithm object in the differential value table based on the secondinput correlithm object comprises: determining Hamming distances betweenthe second input correlithm object and the second correlithm objects inthe differential value table; and identifying the second correlithmobject in the differential value table associated with the smallestdetermined Hamming distance between the second input correlithm objectand the second correlithm objects in the differential value table. 19.The computer program of claim 18, wherein determining a third correlithmobject from the differential value table comprises determining a thirdcorrelithm object from the differential value table based on theidentified first correlithm object and the identified second correlithmobject.
 20. The computer program of claim 19, wherein determining afourth correlithm object in the amplifier value table based on thedetermined amplifier value correlithm object comprises: determiningHamming distances between the amplifier value correlithm object and thefourth correlithm objects in the amplifier value table; and identifyingthe fourth correlithm object in the amplifier value table associatedwith the smallest determined Hamming distance between the amplifiervalue correlithm object and the fourth correlithm objects in theamplifier value table.
 21. The computer program of claim 20, whereindetermining an output correlithm object from the amplifier value tablecomprises determining an output correlithm object from the amplifiervalue table based on the identified fourth correlithm object and thedetermined third correlithm object.